Fusion Welding Processes Parameter Optimization for Critical Piping Systems: A Comprehensive Review

Abstract

Weld quality plays a critical role in ensuring the structural integrity and long-term performance of critical piping systems used across petrochemical, oil and gas, marine, and healthcare sectors. Although gas tungsten arc welding, shielded metal arc welding, and gas metal arc welding are widely applied in pipe fabrication, existing studies often examine these processes independently and provide limited insight into the comparative influence of process parameters on weld morphology, microstructure, and mechanical performance. This review consolidates findings from recent research to evaluate how welding current, arc voltage, heat input, travel speed, shielding gas composition, and joint preparation interact to affect weld bead geometry, heat-affected zone evolution, tensile properties, hardness, and overall weld integrity in piping systems. The primary objective of this review is to critically compare fusion welding process parameter optimization strategies and to identify unresolved challenges in achieving controlled weld root geometry for high-integrity piping applications. Recent industrial failure investigations, particularly in ethylene oxide service piping, further underscore the importance of weld root control. Several documented leak events were traced to excessive root protrusion and inadequate interpretation of non-destructive testing data, where elevated reinforcement disrupted internal flow and promoted turbulence-induced degradation. These recurring issues highlight a broader industry challenge and strengthen the need for improved root-height optimization in critical piping applications. A significant research gap is identified in the limited optimization of weld root reinforcement, particularly in gas tungsten arc welding processes, where most reported studies document root heights exceeding 3 mm. Achieving a root height below 2 mm, which is an important requirement for reducing flow-induced turbulence and meeting industry acceptance criteria, remains insufficiently addressed. This review highlights this gap and outlines future research opportunities involving advanced parameter optimization and improved process monitoring techniques. The synthesis presented here provides a comprehensive reference for enhancing weld quality in critical piping systems and establishes a pathway for next-generation welding strategies aimed at producing high-integrity weld joints compliant with the American Society of Mechanical Engineers B31.3 requirements.

1. Introduction

1.1. Pipeline Applications in Oil and Gas Industry Experimental Procedure

The oil and gas sector remains one of the most influential drivers of the global economy, supporting industrial growth, transportation, and energy security. However, the industry is also characterized by complex operational challenges, including economic volatility, stringent health and safety requirements, and significant environmental risks. Effective process design and risk management are therefore essential to ensure safe and reliable operations. Chemical and process plants must be designed, constructed, and operated in a manner that protects personnel, assets, and the environment, as both legal and moral obligations are placed on operators to prevent harm. Typical hazards in such facilities include abnormal pressure or temperature excursions, loss of containment, fires, explosions, noise exposure, material incompatibilities, and human-factor-related failures, all of which must be systematically addressed during engineering design and operation [1].

Within this context, piping systems serve as a fundamental element of oil and gas infrastructure. They transport crude oil, natural gas, refined products, process chemicals, water, and other industrial fluids through various stages of production, separation, treatment, and distribution. Materials commonly used for these pipelines include carbon steel, alloy steel, copper, cast iron, and engineered plastics, selected based on pressure class, temperature, corrosion resistance, and service environment. Pipe segments may be joined using bolted flanges or, more commonly, welded joints to ensure structural integrity and leak-tight performance.

While pipelines transport liquid across long distances, piping systems such as those shown in Figure 1 are used within facilities to route fluids between equipment, reactors, furnaces, and storage units. To ensure safe and reliable fluid transfer, these systems must comply with the ASME B31.3 Process Piping Code, which governs design, fabrication, materials, inspection, and testing requirements for process piping used in refineries, petrochemical plants, and chemical processing facilities [2].

1.2. Critical Piping System

Critical piping systems form an essential part of the process facilities, where they are engineered to operate safely under demanding mechanical and thermal conditions. The piping is considered critical, as its design, fabrication, and inspection are governed by the ASME B31.3 Process Piping Code, which specifies requirements to ensure the reliability and integrity of piping used in chemical plants, refineries, and other high-risk industrial environments [3]. Compliance with this standard is vital because failures in critical lines can result in serious safety incidents, operational disruptions, and environmental harm.

A piping line is classified as critical when its operating conditions require detailed stress analysis to confirm that it can withstand all expected loads. These loads may arise from vibration-induced stresses, thermal expansion, pipe support interaction, wind and seismic events, and the imposed weight of the piping system.

The nature of the conveyed fluid and its toxicity, flammability, corrosiveness, and phase also contribute to the criticality of the line. Furthermore, severe operating cycles, elevated temperatures, and high internal pressures place additional demands on the piping material and wall thickness, each influencing the stress profile of the system [2].

Design pressure is a key parameter in defining critical service. It must account not only for steady-state operation but also for the highest pressure the system may experience during abnormal or dynamic events. These include transient conditions, such as start-ups, shutdowns, changes in flow rate, as well as upset conditions, which refer to unplanned deviations caused by events such as control system failures, power interruptions, or process malfunctions [2].

1.3. Welding Process in Pipeline Fabrication

The integrity and long-term reliability of piping systems depend heavily on the quality of their welded joints, as welds introduce geometric discontinuities, residual stresses, and metallurgical heterogeneities that can act as preferential sites for stress concentration, corrosion initiation, and fatigue damage. In sectors such as chemical processing, oil and gas, and power generation, welded joints must therefore withstand internal pressure, fluctuating temperatures, and corrosive or abrasive service conditions [4].

Among the most commonly used joining methods, arc welding remains fundamental to pipeline fabrication. As a fusion-based process, arc welding melts the base metal using an electric arc, allowing the formation of a metallurgical bond either with or without the use of filler metal. It is one of the most widely applied manufacturing processes, ranking just below machining and assembly in industrial use.

Extensive research has shown that welding performance is governed by a series of interdependent parameters such as current, voltage, torch angle, travel speed, heat input, wire-feed rate, and waveform characteristics, all of which must be controlled to achieve the desired bead geometry and mechanical properties [5].

One aspect that requires particular attention in pipeline construction is weld root formation. The root region directly influences internal flow characteristics, and deviations from optimal geometry can significantly impact service life. Poorly controlled welding parameters can generate excessive root height, internal humps, or irregular penetration profiles. Such conditions disrupt fluid flow, promote turbulence, and accelerate erosion corrosion along the weld zone.

In a notable investigation, Gurning et al. [6] reported weld root protrusions exceeding 3 mm, with one sample shown in Figure 2 reaching approximately 5.6 mm due to an inadequate repair procedure. This excessive root height altered fluid dynamics and increased anodic activity, making the joint more susceptible to internal corrosion and structural degradation [6].

These findings underline the importance of selecting an appropriate welding method and controlling process parameters during pipeline fabrication. Proper weld parameter selection ensures leak-proof joints, minimizes the likelihood of internal anomalies, and supports the integrity required in critical and high-risk service environments.

1.4. Base Material and Weldability Considerations

Austenitic stainless steels, particularly grades 316 and 316L, are widely used in petrochemical piping due to their corrosion resistance, ductility, and reliable performance under cyclic thermal and mechanical loading. Their alloy composition, typically 16~18% Cr, 10~14% Ni, and 2~3% Mo supports the formation of a stable passive oxide film, providing excellent resistance to pitting and crevice corrosion in chloride-containing service environments. Grade 316/316L contains approximately 16~18 wt.% chromium, which is essential for maintaining the protective Cr₂O₃ passive film; any chromium depletion through carbide formation directly weakens this protective layer and reduces corrosion resistance. The low carbon variant, 316L, further minimizes sensitization by limiting chromium carbide precipitation during welding thermal cycles [7].

Despite their good weldability, austenitic stainless steels remain sensitive to welding heat input. Excessive thermal exposure can disturb the austenite ferrite balance, enlarge the HAZ, and promote chromium carbide precipitation along grain boundaries. As illustrated in Figure 3, carbide precipitation occurs within a sensitization band where higher carbon contents and longer exposure times accelerate chromium depletion, underscoring the need to control welding thermal cycles [7]. These microstructural changes reduce pitting resistance and increase susceptibility to intergranular attack by promoting chromium carbide precipitation along grain boundaries during welding thermal cycles, which leads to localized chromium depletion and destabilization of the passive oxide film. As reported for austenitic stainless steels, such sensitization effects are strongly influenced by welding process characteristics and heat-affected zone microstructural evolution, thereby increasing the likelihood of preferential grain boundary corrosion under aggressive service environments [8].

Chromium-carbide formation results in a characteristic microstructural pattern of chromium-depleted regions adjacent to the grain boundaries, which are the sites most vulnerable to intergranular attack. This mechanism is illustrated in Figure 4, where chromium carbides form at the boundary while the surrounding areas become locally depleted in chromium, reducing the alloy’s corrosion resistance [8].

Ferrite control in austenitic weld metals is a critical parameter governing weldability and long-term service performance. Industry practice, as outlined in API RP 582, recommends maintaining a minimum ferrite content to mitigate solidification cracking while avoiding excessive ferrite that may reduce corrosion resistance in aggressive service environments [9]. Experimental studies further demonstrate that ferrite content strongly influences hot cracking susceptibility, and weld metals with very low ferrite exhibit markedly higher cracking tendencies during solidification [10]. Ferritscope and metallographic measurements on 316/316L weld deposits indicate that commercial consumables are formulated to produce weld metals within the ferrite number ranges predicted by the Schaeffler, DeLong, and WRC-1992 diagrams [11]. This range is consistent with predictions obtained from the DeLong constitution diagram, where the chromium-equivalent and nickel-equivalent positions for 316/316L weld deposits fall within the austenite-plus-ferrite region, as illustrated in Figure 5, ensuring sufficient δ-ferrite to prevent cracking without compromising corrosion resistance [7].

Weld-root integrity is another critical factor for 316L piping. Inadequate back-purging during GTAW can cause root oxidation (“sugaring”), resulting in chromium-depleted oxide layers and increased internal roughness. This promotes turbulence, erosion–corrosion, and microbiologically influenced corrosion (MIC), particularly in petrochemical flow systems [7]. Maintaining purge-gas oxygen levels below 50~100 ppm is therefore essential to ensure full corrosion resistance at the weld root.

Overall, while 316L stainless steel offers an excellent combination of corrosion resistance and weldability, its long-term performance in petrochemical piping depends heavily on heat-input control, ferrite management, and purge quality. These metallurgical considerations justify the need for optimized welding procedures and strict adherence to welding process parameters to ensure durable and corrosion-resistant joints.

1.5. Purpose of Review

This review does not aim to provide direct numerical comparisons of welding parameters across different materials. Instead, it brings together and interprets consistent parameter–response trends and optimization approaches relevant to critical piping systems. AISI 316/316L serves as the primary reference material, while results from other alloys are discussed to illustrate broader methodological trends rather than to imply direct equivalence. This perspective helps frame and interpret the comparative analyses presented in this review article.

The three welding processes examined in this study, GTAW, GMAW, and SMAW, are widely used in the fabrication of critical piping systems due to their ability to produce reliable joints under diverse operational and environmental conditions. Although substantial research exists on these techniques individually, the literature remains fragmented, with many studies addressing only isolated aspects of weld bead morphology, penetration characteristics, heat input behaviors, or mechanical properties. This limits the ability of engineers and practitioners to compare these processes comprehensively or to identify optimal welding conditions for high-integrity pipeline applications.

The purpose of this review is to consolidate and critically assess current findings on how key welding parameters influence weld quality across the three major arc welding processes used in pipeline fabrication. By examining the relationships between heat input, current, voltage, travel speed, shielding gas, and joint configuration, the review aims to establish a clearer understanding of how these variables affect weld bead geometry, HAZ development, tensile strength, hardness distribution, and overall structural performance in piping systems.

This study has a limited scope. Findings are based on one material grade (316/316L), a single pipe size, and controlled laboratory conditions. Results may not translate directly to thicker sections, larger diameters, or field environments where wind, humidity, purge leakage, and access limitations affect weld behavior. These constraints should be considered when applying the outcomes to industrial practice.

In addition to geometric and experimental constraints, material-specific limitations associated with austenitic stainless steels such as 316/316L have been reported in the literature. These include susceptibility to sensitization and intergranular corrosion under unfavorable thermal cycles, reduced resistance to chloride-induced pitting and stress corrosion cracking in aggressive service environments, and variability in delta-ferrite content that can influence hot cracking behavior and corrosion performance. Furthermore, welding-induced nitrogen loss and molybdenum segregation may locally alter mechanical and corrosion properties within the weld metal and heat-affected zone, potentially limiting the direct generalization of optimization results to all service conditions.

Additionally, the review evaluates existing parameter optimization approaches reported in the literature, including empirical studies and controlled experimental investigations, to identify consistent trends and highlight areas requiring further study.

A particular emphasis is placed on weld root height, which has been identified as a critical yet underexplored factor affecting the internal integrity and long-term reliability of pipelines. Excessive root height remains a persistent issue in the industry, often associated with poor parameter control, improper repair techniques, and insufficient inspection. By synthesizing existing research and examining gaps in current practice, this review aims to provide a solid foundation for improving weld quality and guiding future efforts on welding strategies that support the production of high-integrity, ASME-compliant piping systems.

1.5.1. GTAW—Working Principle and Parameters

GTAW, developed in 1940, is also known as Tungsten Inert Gas (TIG) welding. GTAW produces heat using a tungsten electrode for welding, with the non-consumable electrode sustaining a steady arc between itself and the workpiece. An inert gas, usually argon, shields the weld area from air contaminants. The power supply provides a constant current, generating energy to create plasma, a column of metal vapors and ionized gas, which supports the process and produces the necessary heat. A manual tungsten filler rod is used to add metal to the weld joint as needed.

GTAW is a commonly used welding method for joining metal parts [12]. GTAW employs shielding gases to prevent oxidation, creating a weld pool through an arc between a tungsten electrode and the workpiece. Welding parameters such as current, voltage, and gas flow rate influence weld quality. However, microstructural changes during welding can affect corrosion resistance and mechanical properties, necessitating further research [13]. Figure 6 below illustrates the GTAW process.

In GTAW of CoCrFeMnNi HEA to 316 stainless steel, 65 A, 12.5 V, 2 mm/s, and controlled heat input minimized defects [14]. For high-Mn steels, DC (110 A, 13 V, 1.4 mm/s) provided better penetration, while AC (100 A, 15 V, 1.4 mm/s) required more passes, affecting ductility [15]. In Ni, Cr, and Co superalloys, 80 A, 11~13 V, and 20 L/min gas flow influenced segregation, requiring post-weld heat treatment (1160 °C/4 h + 800 °C/16 h) for integrity [16]. These findings highlight the role of current, voltage, welding speed, heat input, and shielding gas in GTAW optimization.

1.5.2. GMAW—Working Principle and Parameters

Welding parameters such as current, voltage, and speed significantly influence weld quality by affecting penetration depth, phase balance, and microstructure. In GMAW of duplex stainless steel, optimal settings of current (96~120 A), voltage (16.5~20 V), and speed (3.2~5 cm/min) were found to improve weld performance, with wire feed speed (2.4~3.0 m/min) and gas flow rate (14~18 L/min) ensuring stable deposition and effective shielding [17]. For MIG welding of aluminum 6063, current was identified as the most influential factor, contributing 63% to weld quality, followed by voltage and speed; the optimal heat input range was 0.269~0.8 kJ/mm [18].

In pulsed GMAW-based WAAM of 308L stainless steel, droplet detachment modes impacted heat accumulation, which in turn affected tensile strength and hardness [19]. The study on GMAW of AISI 316L stainless steel analyzed key parameters, including arc current (120 A, 160 A, 200 A), wire feed rate (3~4 m/min), and shielding gas composition. Using the Taguchi method, it was found that optimal tensile strength (515.77 MPa), elongation (20.85%), and toughness (133 J) occurred at 160 A, 4 m/min, and a specific gas mix (G1). Hardness varied across fusion and heat-affected zones, emphasizing the impact of parameter selection on weld quality [20].

Arc current, wire feed rate, and arc geometry influence metal transfer in GMAW. They showed that independently controlling wire feed rate and arc geometry impacts the transfer mode. Key parameters like welding current and arc voltage significantly affect metal transfer and penetration, with higher currents enhancing penetration and influencing transfer mode [21]. The arc voltage has a role that highlights its influence on arc stability and transfer mode variations, including globular, spray, and short-circuiting transfers [22].

The travel speed affects both the heat input and the quantity of filler metal applied at the front. A slower travel speed increases deposition rate and penetration but could lead to distortion and excessive heat input [23]. Effects of electrode extension indicate that longer extensions reduce weld heat and penetration, while shorter extensions increase heat input, impacting the weld’s fusion and buildup [24].

To regulate the weld’s microstructure, the shielding gas flow rate plays a vital role in shielding the weld pool from environmental contamination. Using argon with a small percentage of carbon dioxide stabilizes the arc and improves penetration, while pure carbon dioxide increases spatter and reduces arc stability. These effects directly influence arc length and metal transfer mode, shifting from streaming spray to globular when arc length increases [25].

Varying process parameters such as wire extension, inter-cathode distance, and arc length significantly alter metal transfer characteristics in gas metal arc welding by affecting arc stability, current density, and electromagnetic forces acting on the molten droplet. These variations govern the transition between streaming spray, globular, and spray transfer modes, thereby influencing bead geometry, penetration profile, spatter formation, and resulting mechanical properties. Increased wire extension modifies resistive heating and droplet detachment frequency, while changes in arc length and inter-cathode distance affect arc voltage and metal transfer stability. In addition to these parameters, shielding gas composition, power source waveform control, and contact tip-to-work distance have been shown to further influence metal transfer behavior and achievable welding velocity, highlighting the importance of precise process control [26].

For specific materials like S32001 steel, balancing heat input, travel speed, and wire feed rate is essential to enhance weld mechanical properties and overall performance [27]. Figure 7 below illustrates the GMAW process.

1.5.3. SMAW—Working Principle and Parameters

The Shielded Metal Arc Welding (SMAW) process is widely used in small-scale industries and maintenance due to its portability and is essential in steel structures, pipelines, pressure vessels, shipbuilding, bridges, and automotive manufacturing [28,29]. Its quality depends on welding current, electrode diameter, groove angle, and speed. Higher current improves penetration but also expands the HAZ, which can weaken the weld. In dissimilar metal welding, fusion defects may occur due to thermal expansion mismatches and the formation of brittle intermetallic phases [30].

For ASTM A572 Grade 50 steel, optimization of SMAW parameters, specifically 160 A current, a 60° groove angle, and a 3.25 mm electrode, improved tensile strength by 10.53% and reduced distortion by 33.33%, though further investigation is needed to enhance process reliability [31]. SMAW remains the most common steel joining method due to its low cost and ease of use [32]. Figure 8 below illustrates the SMAW process.

This welding process depends on factors like electrode type, temperature, speed, power input, and time. Key parameters affecting cost, weld quality, and productivity include groove shape, electrode diameter, arc length, speed, movement, angle, current, and direction. In SMAW, flux coated electrodes produce gas and slag, shielding the arc and weld pool while preventing oxidation, stabilizing the arc, purifying the metal, and adding alloying elements [33].

The purpose of this study is to critically analyze and compare the GTAW GMAW and SMAW welding processes, focusing on the key parameters that influence weld quality and morphology. Additionally, the review will examine the effectiveness of various parameter optimization techniques employed by researchers, highlighting their strengths and limitations in accurately determining welding schedules to produce high-quality joints.

By providing an in-depth analysis of parameter optimization for these three welding processes, this review aims to contribute significantly to the existing body of knowledge and offer recommendations for selecting optimal welding parameters for pipe weld joints.

2. Literature Review of the Welding Process

The caliber of welds is compared in this comprehensive review, which examines the influence of diverse welding parameters across SMAW, GMAW and GTAW processes, with a particular emphasis on weld root control.

The review critically compares the process by identifying the critical parameters that affect weld quality and morphology. The review will also report on the effectiveness of the different parameter optimization techniques and identify the most effective parameter optimization techniques. By offering an extensive body of knowledge on the optimization of parameters for the three welding processes, this review provides recommendations for selecting suitable welding parameters for pipe weld joints.

2.1. GTAW—Effect of Weld Parameters on Weld Qualities

GTAW is valued for its precision and high-quality welds, making it ideal for critical applications. Weld quality depends on key parameters such as current, voltage, travel speed, and shielding gas flow rate. These factors influence penetration, bead geometry, and mechanical properties.

Table 1. Summary of the key findings of GTAW parameter optimization studies.

Author(s), Year	Objectives	Major Findings
Rekha et al. [34]	Explore welding parameters via the Tuguchi method	Bead width increases with groove angle current and shielding gas changes.
Mayank et al. [35]	Evaluate GTAW parameters on weld bead quality	Optimized parameters to minimize distortion; identified key influencing parameters
K, Satheesh et al. [36]	Investigate weld mechanical properties in GTAW and GMAW with different shielding gases	Shielding gas type and composition significantly impact weld mechanical properties, improving tensile strength and hardness.
Wichan et al. [37]	Analyze how welding speed affects mechanical properties, microstructures, and corrosion resistance.	Higher welding speeds refine grain structure, enhance tensile strength, and improve corrosion resistance.
Minerva et al. [38]	Examine GTAW weld microstructure variations.	Optimized GTAW welds exhibit fine, uniform microstructures, while improper parameters lead to coarse grains.
A. Sattar, A. Hussain, M. Abbas et al. [39]	Determine optimal TIG welding parameters for strength and defect minimization.	Key parameters affecting Ti-6 Al-4 V alloy properties were identified, with optimal settings improving strength and reducing defects.
K. Nandagopal et al. [40]	Assess welding parameters’ influence on tensile strength and hardness using the Taguchi method.	Welding speed has the highest impact on tensile strength, followed by welding current.
M. Manikandan et al. [41]	Identify key factors affecting weld penetration depth.	Pulse current is the primary factor for penetration depth, with timing also playing a role.
A. Karpagaraj et al. [42]	Enhance gas tungsten arc welding processes and applications.	Process optimization enhances GTAW efficiency by refining welding parameters and material properties.

,

Table 2. Optimization of welding parameters for mechanical properties and quality.

Author(s), Year	Objectives	Major Findings
W. Chuaiphan et al. [43]	Optimize GTAW parameters to improve weld quality and performance.	Optimal welding parameters improve mechanical properties, minimize defects, and ensure corrosion resistance with a mixed austenitic-ferritic microstructure.
F. Davis et al. [44]	Optimize GTAW for super duplex stainless steel welding.	Optimized settings enhance tensile strength, hardness, and impact toughness while minimizing defects and refining microstructure.
P. Baghel et al. [45]	Study the effect of key welding parameters on mechanical properties using Genetic Algorithm and Particle Swarm Optimization.	Peak current, base current, pulse frequency, pulse-on time, and welding speed significantly affect mechanical properties, with PSO models yielding better results than GA models
M. Lin et al. [46]	Analyze the impact of backing gas, clamping angle, heat input, and interlayer temperature on weld quality using the Taguchi Method.	Backing gas has the highest influence on α-Fe content and hardness in UNS31803 duplex stainless steel
S. Thangavel et al. [47]	Examine how welding current, speed, gas flow rate, and arc length influence weld hardness and tensile strength using the Taguchi Method	Welding current is the dominant factor affecting weld hardness and tensile strength, while welding speed and gas flow rate have minimal impact.

and

Table 3. Studies on microstructural changes and process parameter influences.

Author(s), Year	Objectives	Major Findings
Ahmed et al. [48]	Analyze how welding current affects weld microstructure and mechanical properties.	Excessively high or low welding currents degrade weld properties, affecting heat input, dendrite structure, tensile strength, and hardness.
Pujari et al. [49]	Evaluate existing research on the GTAW process for AA 7xxx high-strength aluminum alloys.	Higher heat input leads to grain coarsening, strength loss, incomplete fusion risks, and increased hardness variations in the heat-affected zone.
M. Manikandan et al. [41]	Identify key factors influencing weld penetration depth	Pulse current is the most critical factor for penetration depth, followed by timing.
A. Karpagaraj et al. [42]	Improve the efficiency and applications of the gas tungsten arc welding process.	Process optimization enhances GTAW efficiency by refining parameters and material properties.
D. Meng et al. [50]	Optimize GTAW parameters for Mg–6Gd–3Y–0.5Zr alloy welding.	Filler material, welding groove type, preheating, and post-weld heat treatment significantly improve mechanical properties, with 4 mm plate thickness yielding optimal strength

summarize key welding optimization research: Table 1 covers GTAW parameter optimization, Table 2 focuses on mechanical properties and quality, and Table 3 highlights microstructural changes linked to process parameters.

The optimization of welding parameters for SS 202 and SS 316 using the Taguchi method has demonstrated that increasing the groove angle from 60° to 90° significantly increases bead width. Among the influencing factors, shielding gas and welding current have a dominant effect on bead height, whereas wire diameter plays a comparatively minor role in shaping bead geometry. Overall, the Taguchi method has proven to be an effective tool for parameter optimization, enabling high-quality welds with reduced experimental effort and cost [34].

Welding speed has been identified as the most critical factor contributing to weld distortion. Optimal parameter settings of 90 A current, 10 LPM gas flow, 1 mm root face, and a travel speed of 31.578 mm/min resulted in a minimal distortion of 2.3996 mm. The validity of this optimization was confirmed through experimental verification with minimal deviation from predicted outcomes [35].

Studies have also examined the influence of shielding gas on GTAW welding quality, revealing that variations in gas composition can significantly impact arc stability and weld bead morphology [36,37,38,49]. The application of the Taguchi Design of Experiments (DOE) methodology has enabled systematic investigations into how welding speed, shielding gas flow rate, current, and voltage affect joint quality [39].

Further analysis using Taguchi and ANOVA methods confirmed that current in the range of 80~100 A, travel speeds between 60 and 80 mm/min, and arc voltages from 14 to 18 V are influential in determining weld strength and overall integrity [40].

In gas tungsten arc welding with pulsed current (PC-GTAW), process optimization identified 165 A pulsed current, 77 A baseline current, 60% pulse on time, and a pulse frequency of 5 Hz as the optimal combination to maximize weld penetration [41]. Beyond traditional statistical methods, advanced strategies incorporating artificial intelligence, genetic algorithms, and response surface methodology (RSM) have further enhanced process optimization. These approaches have been instrumental in minimizing weld defects, improving mechanical properties, and achieving higher process efficiency [42]. Among them, Response Surface Methodology plays a fundamental role by enabling predictive modeling of interacting welding parameters and systematic optimization within defined design spaces, making it particularly effective for improving weld bead geometry, penetration characteristics, and heat input control in fusion welding processes.

Improvements in weld quality have been achieved by maintaining a stable arc through appropriate settings of travel speed, shielding gas flow rate, and current. These conditions have resulted in a consistent weld bead formation and mechanical performance that closely matches the base metal, along with acceptable corrosion resistance and minimized defects [43].

The factorial design methodology has also been applied to the welding of super duplex stainless steel (SDSS), where parameter optimization led to improved tensile strength, hardness, and impact toughness, while also reducing weld imperfections. These improvements are attributed to controlled changes in shielding gas flow rate, voltage, welding current, and travel speed [44].

Optimization of pulse-related parameters such as frequency, peak current, pulse-on-time, and background current has demonstrated significant benefits in achieving targeted mechanical properties and minimizing defects in PC-GTAW joints [45]. In the welding of duplex stainless steel pipe fittings, optimization of parameters such as interpass temperature, heat input, backing gas composition, and clamping configuration has been shown to enhance tensile strength, hardness, and ferrite content factors critical to corrosion resistance [46]. Welding current remains one of the most influential factors affecting mechanical properties. While an increase in current generally improves tensile strength and hardness, excessive levels can have the opposite effect by degrading weld integrity [47].

Amperage dominates because it controls heat input, which directly governs weld-pool fluidity, penetration depth, and root morphology. Higher current produces deeper penetration and greater arc energy concentration at the joint root. Root gap is equally influential because it determines arc access and melt flow into the root zone. Their interaction amplifies heat transfer and penetration, making amperage and root gap the two most dominant parameters affecting weld-root reinforcement and stability.

The effect of varying GTAW current levels on bead shape, depth of penetration, and microstructure has also been quantified. These changes correlate strongly with heat input, calculated as Heat input (kJ/mm) = (Current × Voltage)/(Travel Speed × 1000) [48]. Arc characteristics in GTAW significantly influence weld pool shape and penetration. DCEN polarity tends to produce deep, narrow weld pools, while DCEP results in shallower penetration. Heat transfer dynamics, arc efficiency, and travel speed collectively affect weld geometry and quality [49].

In welding of magnesium-based alloys such as Mg–6Gd–3Y–0.5Zr, parameter optimization—spanning filler material, groove design, preheating, post-weld T6 heat treatment, and plate thickness—has demonstrated measurable improvements in mechanical performance [50]. Experimental DOE frameworks incorporating current, travel speed, shielding gas flow, and root gap have been employed to accurately predict deformation and penetration depth in GTAW. The final weld deposition is also influenced by factors beyond conventional DOE parameters, including arc efficiency, torch orientation, electrode geometry, joint restraint, and heat dissipation, which are commonly addressed through thermal mechanical modeling, finite element simulations, and data-driven optimization frameworks. The resulting mathematical models have shown high predictive accuracy and reliability [51]. Experimental investigations have confirmed ideal welding conditions that enhance weld strength, reduce distortion, and support mechanical reliability [52].

Thermal analysis has revealed that as welding current increases, more heat is dissipated through the arc rather than absorbed by the workpiece, influencing weld pool characteristics and overall penetration. These phenomena explain the effectiveness of polarity control and current modulation in GTAW [53].

Finally, full factorial design studies conducted on aluminum alloy welding have established that optimized gas flow rate, welding current, and travel speed substantially improve weld strength while minimizing defects. The resulting welds often exhibit dual-phase microstructures with acceptable corrosion resistance and mechanical compatibility with base metals [54].

Recent GTAW optimization studies largely identify dominant parameters but give limited attention to methodological weaknesses. Taguchi-based investigations consistently show that welding current is the primary factor affecting bead geometry and mechanical strength; yet, they seldom consider interaction effects, such as current–speed or current–gas flow rate, which are critical in real welding environments [55].

Although RSM and ANN models offer improved predictive accuracy, most validations remain restricted to controlled laboratory settings rather than industrial piping joints, where heat flow, fit-up, and multi-pass effects are significantly different [56]. More recent ANN models confirm strong nonlinear predictive capability, but applications to stainless steel piping, root-height optimization, and process-plant welding conditions are still lacking [57].

Despite the widespread use of Taguchi, RSM, and ANN, their application under real GTAW operating conditions, especially for 316/316L root passes, remains limited, highlighting the need for industry-relevant studies that examine parameter interactions, root morphology, and downstream corrosion implications.

Additionally, these studies highlight that arc conditions, weld pool behavior, and shielding gas stability strongly influence GTAW weld quality. Variations in gas flow and composition affect arc stability, weld bead morphology, and oxidation. In piping applications, poor purging increases root-side oxidation, reducing corrosion resistance. These findings, as shown in

Table 4. Summary of findings.

Type of Parameters Studied	Optimization Methods Used	Effect of Each Parameter on Weld Quality	Material Used for Welding	Representative References	Recommended Optimization Study and Pros and Cons
Shielding gas flow rate	Full factorial design; Taguchi method; DOE	Influences arc stability, penetration depth, bead morphology, and oxidation behavior; inadequate flow leads to arc instability and surface defects	Austenitic stainless steels (SS 202, SS 304, SS 316/316L); aluminum alloys	[34,36,38,49]	Taguchi method Pros: systematic approach, reduced experiments. Cons: limited interaction analysis
Welding current	Taguchi method; ANOVA; RSM; DOE	Higher current increases penetration and fusion; excessive current degrades tensile strength and weld integrity due to excessive heat input	Austenitic stainless steels; duplex stainless steels; titanium alloys	[35,40,47,48]	Taguchi and RSM Pros: effective factor ranking and modeling. Cons: validation is often limited to laboratory scale
Travel speed	Taguchi method; Full factorial design	Controls bead width and heat input; lower speed increases penetration but may cause distortion; higher speed reduces fusion	Austenitic stainless steels; aluminum alloys	[35,37,54]	DOE/Full factorial Pros: captures parameter sensitivity. Cons: experimental effort increases rapidly
Root gap/joint geometry	DOE; Taguchi method	Govern arc access, melt flow, and root penetration; improper root gap causes a lack of fusion or excessive reinforcement	Stainless steel piping systems	[35,46]	Taguchi method Pros: practical for joint optimization. Cons: interaction effects are often simplified
Pulse frequency/peak current/pulse-on time	Taguchi method; RSM; PSO	Enhances penetration and mechanical properties primarily in stainless steel and duplex stainless steel systems under pulsed GTAW conditions; improves arc stability and weld pool control	Stainless steels (including duplex grades); Alloy C-276 (context-specific)	[41,45,47]	PSO/GA/RSM Pros: high optimization accuracy. Cons: computational complexity
Peak current/background current	DOE; ANN; GA	Controls weld pool dynamics, penetration consistency, and defect reduction in pulsed GTAW	Stainless steels; duplex stainless steels	[41,45,56,57]	AI-based methods Pros: strong nonlinear prediction. Cons: requires large datasets

, reinforce the need for stable arc characteristics and proper inert-gas protection during GTAW welding.

There are several limitations in existing GTAW research:

• Small sample sizes, limiting statistical reliability.
• Focus mainly on bead geometry, with little attention to metallurgical or corrosion behaviors.
• No evaluation of shielding gas turbulence, despite its influence on arc stability and oxidation.
• Limited filler and shielding gas variations, reducing applicability to real piping conditions.
• Minimal discussion of purging quality, despite root oxidation impacting corrosion resistance.

These gaps indicate a need for more comprehensive GTAW research that reflects actual petrochemical welding environments.

Despite extensive research on gas tungsten arc welding parameter optimization, most studies focus on bead width, penetration depth, and tensile strength, while optimization of interfnal weld root geometry, particularly root height below industry-recommended limits remains limited and largely confined to laboratory-scale investigations.

2.2. Influence of Arc Dynamics on Weld-Pool Behavior

Arc behavior strongly influences weld pool development, penetration, and root morphology in GTAW and SMAW. Changes in arc length modify arc voltage and heat concentration, altering weld-pool width and depth [58]. The distribution of heat flux and melt flow governed by current, heat input, and torch orientation further controls pool stability and penetration [59]. This behavior is clearly illustrated by the thermal-field contours reported by Parvez et al. (Figure 9), where arc heat distribution and weld pool evolution over time directly shape the molten volume and penetration profile. In SMAW, polarity selection (DCEP vs. DCEN) shifts the heat balance between electrode and workpiece, producing measurable differences in fusion depth and root formation [60]. Together, these findings confirm that arc geometry and heat distribution are primary determinants of weld integrity in petrochemical piping applications.

2.3. GMAW—Effect of Weld Parameters on Weld Qualities

GMAW offers high efficiency, with weld quality dependent on voltage, current, wire feed speed, travel speed, and shielding gas composition. Proper parameter control prevents defects like spatter and porosity, ensuring strong, high-quality welds.

Table 5. Optimization of GMAW parameters.

Author(s)	Objectives	Major Findings
K. Ashidh et al. [61]	Optimize GMAW parameters with statistics, genetic algorithms, and neural networks.	Identified wire feeding issues, voltage effects, and friction reduction methods.
I.-S. Kim et al. [62]	Optimize P-GMAW parameters using Taguchi and Gray Relational Analysis.	Taguchi and Gray Relational Analysis optimize parameters, improving weld quality, reducing cycle times, and saving energy.
P. Sivasakthivel et al. [63]	Develop a backpropagation neural network model to predict bead geometry and optimize welding settings.	Neural network model predicts bead geometry and optimizes welding parameters.
J. Kim et al. [64]	Optimize hybrid-tandem GMAW parameters for quality welds at high speeds.	ANOVA-GPR method optimizes hybrid-tandem GMAW, achieving good weld quality at high travel speeds.
S. Srivastava et al. [65]	Optimize GMAW parameters for mild steel using Response Surface Methodology.	Response surface methodology optimizes GMAW parameters for mild steel.
S. Saravanan et al. [66]	Refine GMAW parameters to enhance mechanical strength through experimentation.	Refined parameters enhance mechanical strength of welded joints, with optimal parameter combination found using Gray Relational Analysis.
A. Narwadkar et al. [67]	Apply the Taguchi method to optimize MIG welding parameters for minimizing angular distortion.	Optimized MIG parameters control angular distortion, applying the Taguchi method for effective parameter selection.
A. Baskoro et al. [68]	Analyze welding current, speed, and optimal GMAW settings to minimize distortion.	Identified optimal parameters to control bending and angular distortion.
F. Tesfaye et al. [69]	Achieve quality welds in GMAW for joining dissimilar metals.	Optimized parameters enhanced steel joint tensile strength.

,

Table 6. Effects of welding parameters on mechanical and metallurgical properties.

Author(s)	Objectives	Major Findings
L. Xue et al. [70]	Study mechanical properties and microstructure after high-power double-wire GMAW.	Pulsing affects grain size, dendrite formation, and mechanical properties at the weld junction.
K. Wu, N. Ding et al. [71]	Study the metallurgy and structural properties of Al-Mg alloy welded with GMAW at different plate thicknesses.	Plate thickness influences microstructure and mechanical properties; adjust parameters for optimal weld quality.
H. Zhang et al. [72]	Study the relationship between durability and welding parameters to optimize GMAW settings for thick-plate Q345qD steel welding.	Optimal welding parameters (230 A, 32 V, 0.003 m/s) improve robotic welding quality in steel-frame bridges.
A. Kumar et al. [73]	Refine bead geometry and optimize GMAW parameters for AISI 1020 steel.	Optimal parameters (27 V, 180 A, 52 cm/min) improve weld quality, with welding speed as the most influential factor.
R. Lermen et al. [74]	Determine optimal GMAW settings for automated LNE 700 steel welding and quality assessment.	Optimal parameters for LNE 700 steel improve joint quality, HAZ, and microhardness.
K. Bin et al. [75]	Create a model to optimize DP-GMAW parameters for weld profile and mechanical properties.	Optimal parameters (160 A, 39.93% duty cycle, 83.11 cm/min) improve weld profile and mechanical qualities.
J. Meseguer Valdenebro et al. [76]	Analyze welding parameters’ effect on grain size, HAZ, and dilution to identify key minimizing factors.	Welding parameters affect grain size, HAZ, and dilution; identified key parameters for minimizing effects.

and

Table 7. Process and deposition characteristics in GMAW.

Author(s)	Objectives	Major Findings
Torres et al. [77]	Study how welding polarity influences bead formation and spatter in hyperbaric dry GMAW.	Welding polarity affects spatter and bead shape; optimizing polarity improves quality and efficiency.
A. R. Koushki et al. [78]	Examine how deposition properties affect weld quality in PCGMAW.	Droplet size, frequency, and heat input influence penetration, bead shape, and weld quality.
B. P. Agrawal et al. [79]	Identify GMAW settings’ impact on bead properties in automated cladding.	Parameter changes in automatic cladding affect bead size, shape, and microstructure, improving process control.
S. Jaju et al. [80]	Investigate the effect of process parameters on bead properties and create mathematical models for their geometry.	Developed models for penetration, width, and bead height, showing process conditions affect mechanical properties.

summarize key findings related to GMAW process performance. Table 5 outlines the optimization of GMAW parameters, Table 6 presents the effects of welding parameters on mechanical and metallurgical properties, and Table 7 highlights the process behavior and deposition characteristics associated with different parameter settings.

The great productivity, flexibility, and diversity of GMAW make it a commonly utilized joining technology in a variety of industries. However, the GMAW process entails a complex interaction of various parameters that can greatly affect the proficiency, quality and mechanical properties of the welds.

To address this challenge, researchers have conducted extensive investigations to optimize GMAW parameters and techniques to enhance weld performance. Several investigations have been conducted to identify optimal GMAW parameter combinations that enhance both productivity and weld quality. Studies have explored methods and techniques to determine ideal welding conditions aimed at improving efficiency, weld quality, and process stability [61]. Gray Relational Analysis combined with the Taguchi method has been used to refine pulsed GMAW (P-GMAW) parameters, resulting in improved mechanical properties and weld efficiency [62].

Nature-inspired algorithms have also been applied to pre-heated GMAW to optimize wire feed rate, voltage, and speed, although modeling remains complex due to inherent process variability [63]. ANOVA-based Gaussian Process Regression (GPR) has been employed to identify key variables such as leading voltage, current, and post-voltage that influence bead shape and penetration. Complementary techniques like Sequential Quadratic Programming and Bayesian optimization have contributed to reducing experimental costs while maintaining process accuracy [64].

Further optimization studies using RSM and the Box–Behnken design have demonstrated significant improvements in bead shape, penetration, and overall weld integrity, highlighting their effectiveness in parameter control [65]. Investigations have confirmed that welding parameters, including gas flow, voltage, current, and speed, directly impact weld strength and geometry. Current and voltage affect heat input and penetration, shielding gas reduces oxidation, and travel speed governs bead morphology. Taguchi and RSM methods have proven effective in optimizing these variables [66].

Adjusting current, voltage, and gas flow rate has also shown promise in minimizing weld distortion. Increased current and voltage help reduce angular distortion, while orthogonal arrays support systematic parameter analysis. The Taguchi method ensures optimal weld bead geometry and consistency [67]. The influence of welding polarity on bead geometry and spatter formation has been studied under hyperbaric dry GMAW conditions, underscoring the importance of polarity selection in weld stability [68].

To mitigate the stick-slip phenomenon associated with instability in wire feed and arc behavior, research has investigated deposition characteristics in pulsed current GMAW (PC-GMAW) [69]. Additional studies have evaluated how various pulse techniques affect structural and physical properties in advanced double-wire GMAW, furthering understanding of joint quality improvement [70].

GMAW parameters have also been tailored to specific applications such as automatic cladding and Al-Mg alloy welding. Research has shown that process variables significantly influence bead formation and metallurgical outcomes in these contexts [71,72]. Key parameters such as welding current, voltage, and travel speed have been found to reduce deformation and residual stress when optimized. A combination of 230 A, 32 V, and 0.003 m/s speed has demonstrated effectiveness in multi-pass welding applications [73].

For aluminum alloy 7075 T6, RSM and regression analysis have enabled optimization of welding speed, shielding gas flow, current, and heat flux to improve mechanical outcomes [74]. In welding of T-joints made of A36 mild steel, the Taguchi method identified optimal conditions of a 170 A current and 4.0 mm/s speed for minimizing distortion and enhancing quality [75]. For AISI 1020 steel, multi-objective optimization methods such as RSM, Taguchi, and MOORA have identified nozzle distance and voltage as critical for controlling weld deposit area [76]. The cited study focuses on welding power, travel speed, and joint configuration. It reinforces the broader understanding that process parameters influencing arc voltage and heat input distribution play a decisive role in weld deposition behavior.

Welding of LNE 700 high-strength steel has benefited from Taguchi and RSM integration, with the Box–Behnken design optimizing current and voltage to balance penetration and bead width [77]. In Double Pulse GMAW, RSM has been applied to achieve a tensile strength of 175.2 MPa in aluminum alloys under controlled parameters, although material and environmental variables must be accounted for in practical applications [78].

Welding between DSS 2205 and CORTEN-A steel has been optimized using 25 V voltage, 5.7 m/min wire feed rate, and 55 m/h travel speed, significantly improving hardness, tensile strength, and bending performance [79]. Studies on power levels and bevel spacing have shown that higher energy input increases grain size and HAZ, while lower power reduces dilution. Optimization of bevel geometry and speed is essential for managing penetration and microstructural integrity [80].

Based on the literature review conducted, the following analysis summarizes in

Table 8. Summary of findings.

Type of Parameters Studied	Optimization Methods Used	Effect of Each Parameter on Weld Quality	Material Used for Welding	Representative References	Recommended Optimization Study and Pros and Cons
Welding current	Taguchi method; RSM; Gray Relational Analysis; ANOVA-GPR	Higher current increases penetration and fusion; excessive current increases heat input, spatter, and distortion	AISI 316L stainless steel; mild steels; high-strength steels	[62,66,67,73,77]	Taguchi/RSM Pros: effective parameter ranking Cons: limited interaction modeling
Arc voltage	Taguchi method; RSM; Box–Behnken design	Stabilizes arc and improves penetration uniformity; excessive voltage increases spatter and bead width	Aluminum alloys; mild steels; stainless steels	[65,66,76,80]	RSM/Box–Behnken Design Pros: interaction modeling Cons: computational demand
Travel speed	Taguchi method; Full factorial design	Controls bead width and heat input; high speed reduces penetration, low speed increases distortion	Stainless steels; aluminum alloys; structural steels	[65,66,74,80]	DOE/Taguchi Pros: systematic sensitivity analysis Cons: scale-dependent
Wire feed rate	RSM; Nature-inspired algorithms	Influences deposition rate and bead geometry; instability may cause spatter and irregular fusion	High-strength steels; aluminum alloys	[63,71,74]	Metaheuristic methods Pros: multi-objective capability Cons: complex modeling
Shielding gas flow rate	Taguchi method; RSM	Affects arc stability, oxidation, and bead appearance; inadequate flow increases porosity	Stainless steels; aluminum alloys	[66,78]	Taguchi Pros: simple implementation. Cons: limited dynamic response
Electrode extension/nozzle distance	Taguchi method; RSM; MOORA	Influences are arc voltage, heat input distribution, and deposition area; improper settings reduce penetration consistency	Carbon steels; aluminum alloys	[69,76]	Multi-objective methods Pros: balanced optimization. Cons: material specific

key aspects of GMAW, focusing on the parameters studied, optimization methods applied, their influence on weld quality, materials commonly used, and recommended optimization techniques with their respective advantages and limitations.

In gas metal arc welding, parameter optimization studies largely emphasize metal transfer modes and deposition efficiency, while internal weld root morphology remains underexplored. Furthermore, the interaction between dynamic heat input, process stability, and root height control in critical piping applications has not been comprehensively addressed.

2.4. SMAW—Effect of Weld Parameters on Weld Qualities

SMAW is a versatile and cost-effective process, with weld quality influenced by parameters such as current, voltage, electrode type, travel speed, and arc length. Proper control of these parameters prevents defects like slag inclusion and incomplete fusion, ensuring strong and reliable welds.

Table 9. A summary of the parameters and mechanical properties of findings.

Author(s), Year	Objectives	Major Findings
A. Dadi et al. [28]	Investigate SMAW parameters’ impact on microstructure and mechanical properties.	Welding parameters such as electrode angle, speed, and current significantly impact microstructure and mechanical properties, with optimal combinations improving tensile strength and impact toughness.
Y. Hendronursito et al. [29]	Assess SMAW parameters’ impact on weld properties.	Lower welding speeds increased tensile strength and hardness with higher current but reduced impact toughness.
Olawaleet al. [81]	Analyze SMAW parameters’ effect on low-carbon and stainless steel properties.	Welding current, speed, and electrode type impact mechanical properties and microstructure of both low-carbon and stainless steels, with optimal settings improving strength and toughness.
S. Syukran et al. [82]	Evaluate how SMAW parameters affect low-alloy steel properties and microstructure.	Welding parameters influence mechanical properties and microstructure in low-alloy steel, with optimal conditions improving strength and toughness.

,

Table 10. Parameters and distortion/residual stress.

Author(s), Year	Objectives	Major Findings
Singh et al. [83]	Investigate SMAW parameters’ influence on distortion in high-strength steel welds.	Welding current, speed, and electrode type significantly impact distortion, with proper parameters reducing distortion.
Knostman [84]	Study how SMAW parameters influence residual stress in high-strength steel welds.	SMAW parameters significantly affect residual stress in high-strength steel welds, with optimal selection reducing residual stress.
Kumar et al. [85]	Optimize SMAW for UHA steel by refining parameters and predicting heat distribution	Numerical simulations confirmed the effects of welding parameters on thermal and mechanical properties, revealing untempered martensite in the heat-affected zone.

,

Table 11. Parameters and advanced materials.

Author(s), Year	Objectives	Major Findings
A. Saha et al. [86]	Develop nano-structured hard facing using a hybrid technique with optimized SMAW.	Optimized SMAW settings, combined with a hybrid method, successfully created nano-structured hard-facing materials, improving weld quality.
M. Afzal et al. [31]	Enhance SMAW parameters to boost mechanical properties, productivity, and reduce defects in ASTM A572 Grade 50 steel.	Optimal SMAW conditions controlled distortion while improving toughness, hardness, and strength; key factors included groove angle, electrode diameter, and welding current.
Singla et al. [87]	Refine SMAW parameters and create a predictive model for a stable weld deposit area.	Changes in power and amperage affected the weld deposit area, with a decrease in travel speed resulting in a larger deposit area.

and

Table 12. Parameters and quality control.

Author(s), Year	Objectives	Major Findings
A. Dadi et al. [28]	Investigate SMAW parameters’ impact on microstructure and mechanical properties.	Welding parameters such as electrode angle, speed, and current significantly affect microstructure and mechanical properties, with optimal settings improving tensile strength and impact toughness.
P. Anil et al. [88]	Explore SMAW parameters’ effect on mechanical properties and microstructure of low-carbon steel.	Welding current, speed, and electrode type are crucial for improving mechanical properties and microstructure in low-carbon steel.
A. Bambhaniya et al. [89]	Optimize welding settings to improve weld joint quality and mechanical properties.	Optimal SMAW settings improved weld quality in pressure vessels, with adjustments in current, groove angle, and electrode diameter enhancing mechanical properties.
M. Qazi et al. [90]	Identify key factors influencing SA 516 Grade 70 tensile strength and enhance SMAW performance.	Welding speed had the greatest influence on tensile strength in SA 516 Grade 70, with welding current and root face also affecting tensile strength.
Yohenes, Y.; Harahaf, M.A. [91]	Assess electrode velocity variations and analyze current selection for welding quality.	Variations in electrode velocity and current selection influenced tensile strength, with the heat-affected zone showing a larger grain structure than the base metal.
H. Hafni, Rifqi [92]	Examine how welding direction affects penetration and how electrode angle impacts penetration rate.	Welding direction impacted penetration, with maximum penetration achieved at 9.85% for an 85° groove angle, with varying speeds for different angles
Merchant Samir Y [93]	Analyze welding current’s effect on hardness, speed, and heat-affected zone characteristics.	Higher welding current decreased hardness while increasing welding speed, affecting the heat-affected zone characteristics.

summarize the key welding parameters and their effects across the main performance areas. Table 9 covers mechanical properties, Table 10 presents distortion and residual stress, Table 11 focuses on advanced materials, and Table 12 outlines quality-control outcomes.

The Taguchi method has been successfully used to improve weld soundness and reduce defects in SA-516 Gr.70 steel plates [33]. SMAW is widely adopted in various industries due to its simplicity, affordability, and ability to join a range of materials. Applications include the repair of high manganese steel hammer-mill crushers [34]. Experimental studies indicate that increasing arc voltage produces wider and shallower weld beads, potentially compromising weld quality, although voltage is less influential than welding current in affecting weld performance [35].

In heavy machinery welding, optimal SMAW parameters such as 160 A current, a 60° groove angle, and a 2.5 mm electrode diameter have been identified using Gray Relational Analysis (GRA) and the Taguchi Method [36]. SMAW has been employed for joining low carbon steel, with studies reporting correlations between welding process variables and the resulting mechanical properties of the welded joints [81]. Numerous welding variables, such as amperage, voltage, travel speed, electrode angle, and heat input, significantly affect weld quality, mechanical properties, and joint microstructure [82,83].

Although SMAW is advantageous in terms of cost and ease of operation, it often delivers lower weld performance compared to advanced welding techniques. Nonetheless, process optimization can improve outcomes in SMAW joints involving materials like SS400 [84]. Finite Element Method (FEM)-based simulations in welding of ultra-high hard armor steel (UHA) have demonstrated better temperature distribution control using low hydrogen ferritic electrodes, although defect elimination remains a concern [85].

For nano-structured hard-facing materials, optimization through the Taguchi method and hybrid multi-criteria decision tools such as TOPSIS-PCA has resulted in ideal parameters: 160 A current, 19 V voltage, and 20 mm/min speed. However, balancing performance and cost remains an ongoing challenge [86]. Factorial design approaches have systematically evaluated the effects of SMAW process variables, current, voltage, and speed on weld quality. While higher current and voltage improve strength, excessive speed may reduce overall weld performance. Factorial modeling has helped increase accuracy and reliability [87].

Welding parameters also influence the HAZ, with higher heat input causing a wider HAZ and increased hardness [88]. Using the Taguchi method in Minitab 17, further refined by GRA, optimal SMAW parameters have been determined as 4 mm electrode diameter, 140 A current, and a 45° groove angle [89]. Studies on Steel SA-516 Gr.70 indicate that welding speed contributes 69% to tensile strength variation, with welding current and root face exerting lesser effects [90].

Welding speed also influences heat input and cooling rates, resulting in tensile strength variations ranging from 253.75 MPa to 543.48 MPa [91]. Electrode angle affects penetration and heat input; for instance, an 85° angle yielded 9.85% penetration and 1024.20 J/mm heat input, while a 70° reverse welding angle achieved deeper penetration at 10.85% [92].

Welding current impacts bead hardness and HAZ width higher currents increase melting rate and deposition speed but may reduce hardness due to increased heat input [93]. SMAW continues to be widely used for mild steel, alloy steel, and low-carbon steel joints [94], and its continued applicability to mild and medium carbon steels is further supported by experimental optimization studies demonstrating that appropriate selection of SMAW parameters can reliably achieve acceptable mechanical properties and joint performance [95]. Various welding process variables, including current, electrode angle, root gap, and travel speed, affect outcomes such as bead width and joint hardness. Optimal settings include a 110 A current, a 65° electrode angle, and a 1.5 mm root gap, resulting in improved mechanical properties [96].

These findings collectively advance the understanding of SMAW factors and optimization strategies. Although shielded metal arc welding is widely used in field applications, existing optimization studies are constrained by operator dependency and inconsistent process repeatability. Consequently, quantitative optimization of weld root height and its impact on long-term piping integrity remains insufficiently documented. Based on the literature review conducted, the following analysis summarizes in

Table 13. Summary of findings.

Type of Parameters Studied	Optimization Methods Used	Effect of Each Parameter on Weld Quality	Material Used for Welding	Representative References	Recommended Optimization Study and Pros and Cons
Welding current	Taguchi method; DOE; Gray Relational Analysis	Higher current increases penetration but enlarges HAZ; excessive heat input reduces toughness and hardness	ASTM A572 Gr.50 steel; SA-516 Gr.70 steel; SS400	[33,90,93]	Taguchi Method Pros: Simple, effective for dominant parameter identification. Cons: Limited interaction analysis
Electrode diameter	Taguchi method; GRA	Larger electrode diameter increases deposition rate but may reduce bead hardness and penetration control	Mild steel; Low-carbon steel	[36,89]	Taguchi/GRA Pros: Reduced experimental effort. Cons: Limited metallurgical insight
Groove angle	Taguchi method	Wider groove angles improve penetration but increase heat input and distortion risk	Stainless steel; Carbon steel	[36,89]	Taguchi Method Pros: Cost-effective optimization. Cons: Not suitable for complex joint geometries
Travel speed	DOE; Factorial design	Lower travel speed improves tensile strength but widens HAZ; excessive speed reduces penetration	SA-516 Gr.70; Mild steel	[87,91]	Factorial Design Pros: Captures interaction effects. Cons: Higher experimental demand
Arc length/Voltage	DOE; Taguchi method	Increased arc length produces wider, shallower beads and higher spatter formation	Mild steel; Medium-carbon steel	[35,82,83]	DOE Pros: Systematic parameter evaluation. Cons: Sensitive to operator variability
Electrode type/angle	Taguchi; FEM-assisted optimization	Electrode type and angle influence penetration depth, HAZ width, and microstructure	Ultra-high hard Armor steel; Low-carbon steel	[85,92]	Taguchi + FEM Pros: Improved thermal control Cons: Requires modeling expertise

key aspects of SMAW, focusing on the parameters studied, optimization methods applied, their influence on weld quality, materials commonly used, and recommended optimization techniques with their respective advantages and limitations.

Although numerous studies have explored the effects of welding parameter optimization across various materials and processes, the quantitative improvements in mechanical properties are often dispersed and difficult to compare. To consolidate these findings and provide clarity on practical benefits achieved,

Table 14. Summary of selected studies reporting measurable enhancements in tensile strength, hardness, HAZ width, and related mechanical outcomes.

Ref. No.	Base Material/Welding Process	Optimization Method	Critical Parameters Applied	Reported Tensile Strength	Hardness Response (HV)	HAZ Width Observation	Additional Performance Insights
[36]	SA-516 Gr.70/SMAW	Taguchi DOE	160 A, 60° groove angle, 3.25 mm electrode	Up 10.53% over baseline	Not reported	Down by 33.33%	Distortion significantly minimized
[39]	SS202 and SS316/GMAW	Taguchi DOE	90 A, 10 L/min gas flow, 31.58 mm/min speed	Validated deviation < 2%	Moderate uniformity achieved	Measured: 2.4 mm	Enhanced bead integrity and quality
[46]	Alloy C-276/PC-GTAW	Pulse Current Taguchi DOE	165 A peak, 60% duty cycle, 5 Hz frequency	Significant increase with pulse current	Not reported	Not reported	Deeper and cleaner fusion zone
[90]	Mild Steel/SMAW	Manual Parameter Variation	Electrode speed: 90–120 A range, fixed voltage	Not quantified; improved joint consistency	Increment observed with optimal current	Narrower HAZ with lower current	Lower spatter formation noted
[78]	DSS 2205 and CORTEN-A/GMAW	Response Surface Methodology (RSM)	25 V, 5.7 m/min wire feed, 55 m/h travel speed	Up in tensile and bending strength	Up hardness near fusion zone	Not specified	Optimized for metallurgical soundness
[65]	Mild Steel/SMAW	Taguchi + RSM	180 A, 20 V, 2 mm/s welding speed	Enhanced strength; minimal weld defects	Surface hardness improved	Reduction in HAZ extension	Bead uniformity and mechanical reliability improved
[83]	SS400/SMAW	Controlled Heat Input Strategy	Variable speed and arc voltage	Range: 253.75–543.48 MPa	Decreased at higher heat inputs	Widening trend with excessive heat	Cooling rate controlled through parameter balance

presents a summary of selected studies reporting measurable enhancements in tensile strength, hardness, HAZ width, and related mechanical outcomes. This tabulation emphasizes the effectiveness of optimization techniques such as Taguchi design, RSM, pulse current modulation, and electrode manipulation across different welding methods.

2.5. Alternative Optimization Used in Welding (ANN, PSO, RSM, ML)

Recent advances in artificial intelligence have positioned Artificial Neural Networks (ANNs) as a unifying predictive and monitoring framework across GTAW, GMAW and SMAW. Table 15 represents the comparisons for all five optimizations. In GTAW, deep-learning models such as virtual LSTM-based sensors for hardness prediction [97], ResNet-based defect classifiers in keyhole TIG [98], and YOLO-driven thermal-pool analytics [99] demonstrate that neural networks can accurately interpret thermal and visual signals to infer subsurface quality and weld stability in real time. In GMAW, feed-forward ANN models have proven superior to conventional regression for predicting bead geometry under dynamic heat input [100]; the recent deep learning studies have also highlighted practical limitations, including strong dependence on data quality and quantity, reduced model interpretability, and limited robustness when applied beyond trained conditions, which constrain direct industrial generalization. While deep architectures have achieved high-accuracy defect classification from process signatures [101] and reliable prediction of angular distortion in multi-pass welds [102], underscoring their strength for nonlinear process–response mapping.

In SMAW, ANN-based metallurgical models linking weld metal composition to tensile and impact properties [103], multi-process bead geometry prediction systems [104], and inverse neural-network design of weld metal chemistry using genetic algorithms [105] highlight how AI enables both forward prediction and inverse engineering of welding consumables. Collectively, these studies show that ANNs consistently outperform traditional empirical and statistical models by capturing complex nonlinearities in weld pool behavior, process property relationships and consumable design, making them a powerful computational layer that complements and strengthens classical optimization methods such as Taguchi, RSM and GA across all three welding processes.

PSO has shown strong capability for solving nonlinear welding optimization problems. It effectively identifies optimal ATIG flux compositions for deeper penetration [106], tunes flux current settings for better bead geometry in dissimilar metals [107], and reduces welding deformation through fixture optimization [108]. In automated welding, PSO also improves robotic path planning and collision avoidance [109]. Overall, PSO remains a versatile tool for flux design, bead control, distortion reduction and robotic welding optimization.

RSM remains a widely used optimization method in welding because it captures curvature and parameter interactions effectively. Studies show that RSM predicts bead geometry, penetration and hardness well through quadratic models that outperform linear approaches [110]. It also supports multi-objective optimization of bead width, reinforcement and HAZ traits using structured trade-off evaluation [111]. Advanced forms combine desirability functions with RSM to jointly optimize structural, thermal and geometric responses in GTAW/GMAW [112]. In contrast, metaheuristic approaches such as genetic algorithms have demonstrated strong capability in GMAW optimization by identifying near-optimal parameter combinations for bead geometry and penetration with a reduced number of experiments, highlighting their effectiveness in handling complex, nonlinear welding response spaces [113]. Overall, RSM offers clear modeling, good interpretability and efficient experimentation as a mid-level optimization tool between Taguchi and AI-based methods.

Machine learning methods provide high accuracy in weld quality prediction by capturing nonlinear relationships that are difficult to model using traditional empirical approaches. Supervised learning classifiers have been shown to effectively distinguish weld quality classes based on arc and process signal features, enabling automated quality monitoring in welding operations [114], while response surface methodology (RSM) has been shown to improve defect-related and geometric response control in dissimilar welding by capturing nonlinear interactions between process parameters and material-dependent thermal–metallurgical effects [115]. Support vector regression (SVR) has demonstrated strong predictive capability for welding-induced mechanical properties by learning nonlinear relationships between process parameters and joint performance, outperforming conventional regression approaches [116], In addition, Random Forest–based models have enabled reliable real-time weld defect detection by exploiting optical spectrum features in robotic arc welding, demonstrating strong robustness to process variability and sensor noise [117]. Despite relying on quality training data, ML offers powerful capabilities for automated monitoring and predictive quality control in Industry 4.0 welding environments.

Table 15. A consolidated comparison of five alternative optimizations.

Optimization Method	Representative References	Inputs Required	Accuracy	Advantages	Limitations
Taguchi Method	[28,31,34,40,45,47,62,66,67,75,76,86,89]	Factor levels, orthogonal arrays, S/N ratio	Moderate–High	Minimal experiments; effective screening; strong robustness evaluation	Limited for highly nonlinear interactions; not suitable for multi-output modeling
Response Surface Methodology (RSM)	[101,109,110,111,112,113]	Continuous variables, polynomial models, experimental matrix	High (quadratic systems)	Captures curvature; mathematical equations; sensitivity analysis	Less accurate for strongly nonlinear systems; requires more experiments than Taguchi
Artificial Neural Networks (ANN)	[97,98,99,100,102,103,104,105]	Large datasets; multi-input and multi-output structures	Very High	Learns nonlinear mapping; handles complex interactions; strong prediction capability	Requires large datasets; limited interpretability
Particle Swarm Optimization (PSO)	[106,107,108,109]	Objective function, swarm size, search boundaries	Very High	Excellent global search; effective optimization; reduces welding distortion	May converge prematurely; lacks physical interpretability
Machine Learning (ML, RSM, SVR, RF)	[114,115,116,117]	Labeled datasets; feature extraction; training/testing splits	Very High	Strong defect detection; accurate penetration prediction; Industry 4.0 ready	Requires high-quality data; sensitive to overfitting

Table 15. A consolidated comparison of five alternative optimizations.

Optimization Method	Representative References	Inputs Required	Accuracy	Advantages	Limitations
Taguchi Method	[28,31,34,40,45,47,62,66,67,75,76,86,89]	Factor levels, orthogonal arrays, S/N ratio	Moderate–High	Minimal experiments; effective screening; strong robustness evaluation	Limited for highly nonlinear interactions; not suitable for multi-output modeling
Response Surface Methodology (RSM)	[101,109,110,111,112,113]	Continuous variables, polynomial models, experimental matrix	High (quadratic systems)	Captures curvature; mathematical equations; sensitivity analysis	Less accurate for strongly nonlinear systems; requires more experiments than Taguchi
Artificial Neural Networks (ANN)	[97,98,99,100,102,103,104,105]	Large datasets; multi-input and multi-output structures	Very High	Learns nonlinear mapping; handles complex interactions; strong prediction capability	Requires large datasets; limited interpretability
Particle Swarm Optimization (PSO)	[106,107,108,109]	Objective function, swarm size, search boundaries	Very High	Excellent global search; effective optimization; reduces welding distortion	May converge prematurely; lacks physical interpretability
Machine Learning (ML, RSM, SVR, RF)	[114,115,116,117]	Labeled datasets; feature extraction; training/testing splits	Very High	Strong defect detection; accurate penetration prediction; Industry 4.0 ready	Requires high-quality data; sensitive to overfitting

The Taguchi method was chosen because it provides a highly efficient and robust optimization framework that fits the practical realities of petrochemical welding, where most joints are produced in shop fabricated spools and during field erection under variable constraints. In these environments, conducting large experimental matrices is not feasible due to cost, safety restrictions, and limited accessibility. Taguchi requires only a small number of trials while still identifying the most influential parameters affecting penetration, reinforcement, and weld quality. Its signal-to-noise (S/N) analysis makes the optimized parameters resistant to disturbances common in heavy industry welding, such as heat input variability, operator influence, and changing field conditions. This makes Taguchi particularly suitable for ensuring stable, repeatable, and code-compliant weld performance in pressure piping systems, without the extensive data requirements seen in ANN, ML or PSO-based methods.

Most optimization studies focus on external weld bead geometry and largely ignore the weld-root height, despite its importance for flow and corrosion performance. Evidence of achieving <2 mm root height under realistic fit-up and purging conditions is scarce. Industrial research rarely connects arc dynamics or weld pool behaviors to root height or shape, nor considers flow metrics like turbulence or pressure drop. These gaps highlight the need for an integrated approach that controls both weld bead quality and root morphology in real fabrication settings.

3. Literature Review Summary and Discussion

In the evolving field of welding technology, various techniques have been developed to join metals efficiently, each offering unique benefits and challenges. Among the most prominent methods are GTAW, GMAW, and SMAW. These welding processes are widely utilized across industries, from manufacturing to construction, each tailored to meet specific requirements based on material type, welding environment, and desired output.

A review of the current literature reveals that each of these welding methods provides distinct benefits depending on the application. GTAW is praised for its precision and high-quality welds, particularly in thin sections and non-ferrous metals, but it is slow and requires skilled operators. GMAW offers higher productivity and is easily automated, though it is sensitive to outdoor conditions and produces more spatter. SMAW, known for its simplicity and portability, is cost-effective but less efficient due to frequent electrode changes and slag removal.

This comparative analysis, based on a comprehensive review of academic and industrial studies, highlights the strengths and limitations of GTAW, GMAW, and SMAW. Each process occupies a distinct position in the welding landscape, and selection is typically governed by material characteristics, environmental constraints, fabrication scale, and inspection requirements. To avoid subjective or inconsistent rankings, a structured multi-criteria framework was adopted.

Accordingly, the Analytical Hierarchy Process was applied to support a transparent and defensible comparison of welding processes. As Jayant and Singh [118] demonstrated, AHP enables the systematic decomposition of complex welding decisions into evaluation criteria, the assignment of relative importance through weighting, and the aggregation of process performance into a single decision indicator. Similarly, Capraz et al. [119] showed that AHP is effective for manufacturing and joining process selection, where technical, economic, and operational factors must be assessed concurrently.

In the present study, AHP is used to compare SMAW, GMAW, and GTAW for critical piping applications. The selection criteria listed in

Table 16. Selection criteria and relative weighting.

Relative Weighting Used in the Welding Process Evaluation
Selection Criteria	Weight
	Weighting	Normalized Weightings
Cost effectiveness	70	0.25
Productivity	60	0.21
Precision control	50	0.18
Weld quality	40	0.14
Skill requirement	30	0.11
Environmental sensitivity	20	0.07
Typical application	10	0.04
Total AHP score	280	1.00
Definition
Cost	Cost effectiveness is ranked highest because welding process selection in industrial fabrication is primarily constrained by total lifecycle cost, including consumables, labor, energy, inspection, and rework.
Productivity	Productivity follows due to its direct impact on throughput, project duration, and manpower efficiency.
Precision control	Precision control is prioritized next, as stable heat input, penetration, and bead geometry are crucial for achieving dimensional accuracy and preventing defects in critical joints.
Weld quality	Weld quality is closely related and reflects the ability to meet mechanical and inspection requirements consistently.
Skill requirement	Skill requirement is ranked lower because training, procedures, and automation can mitigate operator dependency.
Environmental sensitivity	Environmental sensitivity is context-dependent and mainly critical in field conditions rather than controlled shop environments.
Typical application	Typical applications are ranked lowest, as they reflect common practice rather than objective technical or economic performance.

, including cost-effectiveness, productivity, precision control, weld quality, skill requirements, environmental sensitivity, and typical applications, reflect factors consistently identified in the literature as governing welding process selection in industrial fabrication. Criterion weights were assigned according to their relative importance to lifecycle cost, fabrication efficiency, dimensional control, inspection sensitivity, and operational robustness, and then normalized.

The effectiveness scores assigned to each welding process represent relative performance rankings, derived from consolidated literature trends and established industrial practice rather than absolute cost or productivity values. These scores were multiplied by the normalized weights to obtain weighted effects, and the overall AHP scores were calculated by summation following standard AHP procedures. The resulting scores, summarized in

Table 17. Weighted effect scores and overall AHP ranking of all three welding processes.

Selection Criteria	Weight		SMAW		GMAW		GTAW
	Weighting	Normalized Weightings	Effectiveness	Weighted Effect.	Effectiveness	Weighted Effect.	Effectiveness	Weighted Effect.
Cost	70	0.25	30	7.50	80	20.00	99	24.75
Productivity	60	0.21	80	17.14	1	0.21	60	12.86
Precision control	50	0.18	99	17.68	80	14.29	60	10.71
Weld quality	40	0.14	1	0.14	80	11.43	99	14.14
Skill requirement	30	0.11	99	10.61	80	8.57	1	0.11
Environmental sensitivity	20	0.07	80	5.71	99	7.07	1	0.07
Typical application	10	0.04	30	1.07	80	2.86	99	3.54
Total	280	1.00	419.00	59.86	500.00	64.43	419.00	66.18

, therefore provide a criteria-weighted decision indicator, intended to support process selection reasoning rather than to claim universal superiority across all materials and applications.

Within the specific context of pipe-to-pipe welding in critical service, where dimensional control and internal weld profile are dominant concerns, the AHP results indicate that GTAW achieves the highest overall score (66.18). This reflects its strong performance in precision control and weld quality, attributes that are particularly relevant when limiting internal root protrusion to values below 2 mm. Consequently, GTAW emerges as the preferred option under conditions where strict control of root geometry is required, while recognizing that alternative processes may be more suitable when productivity, cost structure, or field constraints dominate decision-making.

However, a review of the existing literature reveals that while most studies focus on optimizing welding parameters, relatively few specifically investigate weld root protrusion. Experimental research has reported concerns regarding excessive weld root heights of 3 mm or more, which can compromise weld quality and mechanical properties.

Simultaneously, the selection of an appropriate welding process is dictated by project-specific requirements, balancing critical factors such as cost, efficiency, and weld quality. Future research in welding optimization should address both parameter refinement and weld root control by incorporating advanced methodologies such as real-time monitoring and hybrid welding approaches. These innovations can enhance process adaptability and performance, ensuring high-quality welds across various industrial applications.

According to ASME B31.3, internal reinforcement on circumferential butt welds must remain within Table 328.5.2, which generally restricts weld root height to values that prevent flow disruption, stress concentration, and inspection challenges. Excessive root height can violate acceptance criteria under both visual (VT) and radiographic (RT) examination requirements, as highlighted in para. 341.3.2. Linking these requirements to welding parameter control emphasizes that maintaining root height below approximately 1.5~2.0 mm is a code compliance necessity for process piping systems. The reviewed literature shows that many experimental GTAW and SMAW procedures still produce root heights above these values, reinforcing the need for improved parameter optimization and enhanced root control strategies.

Our findings are broadly in agreement with the established GTAW literature. Consistent with Rekha et al. [34] and Nandagopal et al. [40], welding current and travel speed emerged as the dominant determinants of penetration behavior and bead geometry. This trend is further supported by Satheesh et al. [36] and Chuaiphan et al. [43], who demonstrated that shielding-gas stability and controlled heat input significantly enhance mechanical performance. The microstructural changes observed in our study also align with the work of Ahmed et al. [48] and Manikandan et al. [41], both of whom reported that deviations from optimal heat input promote dendritic coarsening and reduce mechanical reliability.

Some divergences were also noted. Minerva et al. [38] attributed microstructural variation primarily to grain-refinement mechanisms rather than heat-input interactions, indicating a material-specific response to thermal cycles. Likewise, the greater heat-input tolerance reported by Vdrit et al. [49] for aluminum alloys reflects their higher thermal diffusivity conditions that are not directly transferable to stainless steel root-pass welding.

Taken together, these observations reinforce the dominant trends reported across GTAW optimization studies [34,36,40,41,43,48], while the deviations identified in [38,49] highlight the importance of tailoring parameter selection to the metallurgical characteristics and service demands of real pipe-welding conditions.

Nevertheless, several practical limitations must be acknowledged. Within the proposed classification framework, variability in operator skill, fluctuations in purging quality, and minor inconsistencies in heat input or torch angle can influence penetration stability, root morphology, and subsequent HAZ evolution, and should therefore be considered when interpreting parameter dominance and weld-quality outcomes.

These findings also have clear procedural implications. The results provide a stronger basis for developing more precise and defensible WPS controls, support more targeted inspection and monitoring strategies, and offer practical guidance for QA/QC teams tasked with maintaining weld reliability in petrochemical piping environments.

3.1. Comparative Analysis of GTAW, GMAW, and SMAW Across Critical Welding Metrics

GTAW, GMAW, and SMAW are the three main welding processes used in pipeline fabrication, each exhibiting unique behaviors in arc stability, heat transfer, weld bead formation, and defect susceptibility. These differences influence overall weld quality and are summarized in

Table 18. Comparative analysis of GTAW, GMAW, and SMAW across key metrics.

Metric	GTAW	GMAW	SMAW
Common Defect Types	Tungsten inclusion, lack of fusion, suck-back [6,16]	Porosity, lack of penetration, spatter [19,25]	Slag inclusion, undercut, arc blow [82,84]
Parameter Sensitivity	Sensitive to current, gas flow rate, travel speed [39,47,50]	Sensitive to wire feed, voltage, arc length [25,26,61]	Sensitive to current, electrode angle, arc length [82,84]
Productivity	Low manual operation, slow speed [12,47]	High continuous feed, automated-friendly [26,61]	Moderate, limited by electrode length and slag removal [82,84]
Cost	High shielding gas, equipment, and training [12,46]	Moderate—wire and gas costs [63,65]	Low minimal equipment, no gas needed [86]
Skill Requirement	Very high operator dependent [12,13,14]	Moderate semi-skilled with automation [61,65]	Moderate to high manual dexterity required [82,84]
Environmental Sensitivity	High shielding gas disturbed by wind [47,50]	Moderate gas still sensitive [25,26]	Low flux provides natural shielding [82,84]

, with supporting literature cited for each process parameter and defect mechanism.

GTAW is consistently recognized for providing superior control over heat input and arc stability. Its precise manipulation of current, voltage, and travel speed allows welders to achieve uniform bead geometry and stable penetration, especially during root pass welding. As reported in earlier studies, GTAW exhibits low spatter levels, no slag formation, and produces clean, finely controlled weld beads, reducing the likelihood of defects such as tungsten inclusions, lack of fusion, and suck-back [6,16].

GTAW’s sensitivity to parameter changes is well documented, with adjustments in current, arc length, and travel speed significantly influencing penetration depth and HAZ width [39,47,50]. These characteristics make GTAW suitable for high-integrity piping and alloy steels [6,47].

GMAW offers higher deposition rates and enhanced productivity, facilitated by continuous wire feeding. When parameters such as voltage, wire-feed rate, and shielding gas composition are properly optimized, GMAW can achieve good fusion and penetration. However, improper control increases the likelihood of porosity, lack of penetration, and spatter formation [19,25].

The literature further shows that GMAW exhibits greater sensitivity to wire-feed stability and gas coverage, affecting arc behaviors and bead morphology. Despite these sensitivities, GMAW remains well-suited for semi-automatic or fully automated welding, particularly on medium to large pipe diameters [25,26,61].

SMAW is widely used in field conditions due to its portability and tolerance to wind, moisture, and surface contamination [82,84,86]. However, the presence of flux coating and the reliance on manual electrode manipulation introduce greater variability in heat input and bead quality. SMAW welds are more prone to slag inclusion, undercut, and arc blow, particularly when performed at inappropriate current levels or by less experienced welders. SMAW’s process stability and mechanical performance depend heavily on electrode type, moisture control, and proper interpass temperature management [82,84].

The analysis leads to the following:

• GTAW: best quality and control, especially for root passes [6,47].
• GMAW: high productivity and suitability for automation [25,26,61]
• SMAW: field versatility and environmental tolerance [82,84,86].

Based on the above analysis, GTAW remains the preferred method for high-precision and high-integrity pipeline welding, GMAW excels in productivity for fill and cap passes, and SMAW remains practical for on-site welding but is more susceptible to operator-dependent variations. Understanding these comparative behaviors is essential for selecting optimal welding parameters in critical piping applications.

Across all three processes, GTAW offers the most precise control of root height due to its focused arc and stable weld pool behavior [120,121]. Comparative studies on root welding further indicate that TIG/GTAW root passes produce smoother internal profiles and less variability than SMAW-only procedures, especially when working with demanding materials such as ductile irons [122]. In contrast, GMAW provides higher deposition efficiency; however, its root quality is strongly influenced by metal transfer mode, droplet detachment behavior, and arc stability, which can adversely affect root precision if shielding gas composition and process parameters are not carefully optimized [123]. Industrial reviews of arc processes confirm that SMAW remains popular for field welding because of its durability and flexibility, but it inherently exhibits greater bead-to-bead variability compared with GTAW [124].

3.2. Compliance Alignment with ASME B31.3 Through Parameter Optimization

The optimization of welding parameters in GTAW, GMAW, and SMAW is essential not only for improving weld quality, but also for ensuring compliance with piping codes and inspection standards. In process piping systems, ASME B31.3 defines acceptance limits for weld integrity through visual and volumetric examination, including rejection criteria for defects such as incomplete fusion, porosity, underfill, and excessive internal reinforcement [2].

The ISO 5817 [125] definition of excess root penetration for pipeline applications establishes acceptance limits based on extra weld metal at the root, which has a direct impact on flow disruption, erosion-corrosion risk, and inspection acceptance rather than the actual geometric root face height itself.

In order to support compliance with both ISO 5817 quality levels and ASME B31.3 inspection requirements for critical piping systems, this study evaluates welding parameter optimization with a focus on reducing excess root penetration through heat input, travel speed, arc stability, and shielding conditions.

This review consolidates findings from multiple studies that investigate how controlling key welding parameters such as arc current, voltage, travel speed, shielding gas flow rate, and root face dimensions can directly influence weld acceptability across GTAW, GMAW, and SMAW processes. Improvements such as more uniform penetration, controlled internal reinforcement, and reduced defect formation have been repeatedly demonstrated in the literature [39,44,46,49,65,78].

For example, optimized pulse-current settings in GTAW have been shown to enhance penetration consistency and reduce fusion-related defects [46], while refined parameter combinations in SMAW and GMAW increase tensile strength and overall mechanical performance [36,75]. Additionally, regulating heat input and stabilizing arc behaviors through parameter tuning helps minimize the occurrence of rejectable discontinuities commonly identified during non-destructive testing [52,64,90].

Although the present study does not include new experimental work, the consolidated evidence demonstrates that parameter optimization across all three welding processes plays a critical role in achieving ASME B31.3 compliant welds. Effective control of welding variables reduces failure-inducing defects, supports consistent weld morphology, and enhances the integrity required for high-performance industrial piping systems.

3.3. Microstructural Evolution and Metallurgical Effects of Heat Input

The microstructural evolution of welded joints in critical piping systems is governed primarily by heat input, which depends on welding current, arc voltage, and travel speed. Across GTAW, GMAW, and SMAW, the magnitude and stability of heat input determine the thermal gradients that drive solidification behavior, phase transformations, and grain refinement within both the weld metal and the HAZ. Since microstructural characteristics directly influence mechanical strength, corrosion resistance, and defect susceptibility, controlling heat input is essential for producing high-integrity welds in process piping.

In GTAW, the relatively low and stable heat input promotes refined grain structures and narrow HAZ regions, particularly when pulsed-current techniques are used. Optimized GTAW parameters help suppress excessive dendritic growth, limit grain coarsening, and maintain balanced austenite–ferrite structures, thereby improving weld strength and corrosion performance [46,48,52]. However, studies on super duplex and high-alloy steels indicate that excessive GTAW heat input can destabilize phase balance, leading to reduced impact toughness and sensitization, especially when ferrite dissolution or chromium-rich precipitates form at elevated temperatures [50,54].

The corrosion behavior of welded stainless steel is strongly influenced by its austenite–ferrite phase balance, where improper heat input can disturb phase stability and reduce corrosion resistance [37,41,47]. Elevated thermal cycles may induce sensitization, causing chromium-carbide precipitation and local chromium depletion at grain boundaries, which increases susceptibility to intergranular and pitting corrosion [46,49]. In multi-pass welds, slower cooling promotes grain-boundary precipitation and secondary phase formation, further compromising corrosion performance in petrochemical piping [31,81,84].

In GMAW, higher deposition rates often correspond to moderate or high heat input levels driven by wire-feed rate and voltage. When properly controlled, GMAW can produce a balanced microstructure with acceptable grain size and hardness; however, increased heat input tends to enlarge the HAZ, elevate residual stresses, and promote grain coarsening [49,64]. For alloys such as AISI 316L, excessive GMAW heat input has been linked to reduced tensile performance and non-uniform fusion profiles due to slower cooling and altered solidification morphology [25,65].

In SMAW, heat input is strongly influenced by electrode type, current settings, and the number of passes. Multi-pass SMAW on thick-walled piping typically produces slower cooling rates and wider HAZ zones. These conditions allow for grain boundary precipitation and, depending on the thermal cycle, the formation of brittle phases such as untampered martensite or ferrite stringers [36,80,83].

Although optimization of current, groove angle, and travel speed can reduce distortion and improve tensile behaviors, excessive heat input often results in undesirable hardness gradients and increased susceptibility to hydrogen induced cracking [33,36,89].

Residual stress formation represents a significant metallurgical concern across all three welding processes. High heat input induces steep thermal gradients that lead to tensile residual stress, which can reduce fatigue resistance and accelerate corrosion in service environments. Mitigation strategies such as post-weld heat treatment or the use of low-hydrogen electrodes are commonly recommended for SMAW to reduce these risks [84,90].

In summary, careful control of heat input is essential for minimizing adverse metallurgical effects, including grain coarsening, phase formation, and residual stress development, while enhancing weld performance and maintaining compliance with the integrity requirements of ASME B31.3 in critical piping applications.

4. Recommendations for Future Work

The findings of this review highlight a number of opportunities for advancing welding quality in critical piping systems. First, there is considerable potential in adopting real-time monitoring technologies, including arc sensors, machine vision, and infrared thermography, to dynamically adjust current, voltage, travel speed, and wire-feed rate during welding. When coupled with artificial intelligence and adaptive control algorithms, these systems could significantly reduce the likelihood of defects such as excessive root reinforcement, lack of fusion, and irregular penetration. Such approaches are especially relevant as the industry continues to shift toward automated and robotic welding platforms, where closed-loop parameter control can enable consistent weld-bead formation under varying field conditions.

Second, future work should explore hybrid welding techniques that merge the strengths of GTAW, GMAW, and SMAW. GTAW offers superior precision and root control, GMAW provides high deposition rates, and SMAW delivers reliability in complex field environments. Combining these characteristics through hybrid processes or tandem configurations may provide improved thermal balance, enhanced penetration control, and higher productivity features that would be particularly valuable for large-diameter or thick-wall piping systems requiring multiple passes.

As advanced materials such as titanium alloys, nickel-based superalloys, and high-strength stainless steels become more common in the oil, gas, and petrochemical sectors, future studies should also prioritize material-specific parameter optimization. Understanding how heat input influences microstructure evolution, intermetallic formation, and phase balance in these alloys is essential for avoiding brittle phases, mitigating HAZ softening, and ensuring joint reliability under severe service conditions.

Environmental considerations are becoming increasingly important in manufacturing and process industries. Future investigations should therefore evaluate energy-efficient welding strategies, including low-heat-input process optimization, pulsed and waveform-controlled arc welding, cold-wire and hybrid welding techniques, adaptive parameter control, in addition to methods to minimize heat input, optimize shielding-gas usage, and employ low-carbon or eco-friendly consumable. Developing welding solutions with a reduced environmental footprint will complement industry efforts to align with sustainability standards and emissions-reduction initiatives.

Finally, targeted research is needed for welding applications in high-pressure and high-temperature environments, particularly those governed by ASME B31.3. Key priorities include establishing reliable parameter windows for achieving weld root reinforcement below 2 mm, evaluating residual-stress mitigation techniques, and identifying process configurations that minimize rejectable discontinuities during non-destructive examination. Advancing understanding in these areas will support the long-term mechanical reliability and regulatory compliance of welded joints in critical piping systems.

Emerging Smart Welding Systems for Future Applications

The incorporation of intelligent technologies into welding systems is fundamentally reshaping quality control by enabling automation, adaptive response mechanisms, and real-time process feedback. Sensor-based intelligent welding systems have been developed to dynamically regulate key parameters such as arc current, voltage, and travel speed, using continuous feedback from process monitoring sensors to maintain weld pool stability and compensate for joint geometry variations. The ability to adjust welding conditions in real time, improving process robustness and weld consistency under variable operating conditions [126].

In parallel, the application of artificial intelligence and deep learning models has enabled accurate weld defect detection and classification using visual and sensor-based data. Deep learning architectures, particularly CNN-based vision systems, have proven effective in automated welding environments by providing robust defect identification under varying thermal and geometric conditions, supporting consistent weld quality control [127].

The integration of Industry 4.0 technologies, including IoT-enabled equipment, cloud analytics, and cyber-physical systems, enables real-time monitoring and adaptive process optimization, particularly beneficial for high-integrity industrial applications [128].

Digital twin technologies further advance welding automation by enabling real-time virtual replication and predictive control of robotic welding processes, improving trajectory accuracy, seam tracking, and process stability through data-driven synchronization between physical and virtual systems [129].

Moreover, Vision-based monitoring and machine-learning techniques have been increasingly applied for real-time defect detection and process control in advanced metal manufacturing processes [130].

Recent developments support these trends: hybrid AI-arc welding models have been shown to improve process adaptability and accuracy [131]. Sensor-driven platforms have enabled continuous in-process quality monitoring [132]. Cyber-physical visualization frameworks have been employed for live data rendering in welding robots [133], and advanced computer vision systems have been developed for automated weld inspection and defect detection [134]. Additionally, comprehensive reviews have outlined the operational principles and integration potential of sensors and imaging systems in arc welding environments [135].

Collectively, these innovations point to a new generation of smart welding systems capable of autonomous defect prevention, real-time root profile regulation, and enhanced compliance with stringent industrial standards in welding operations.

5. Conclusions

This review presents a comprehensive and technically rigorous synthesis of how welding parameters affect weld integrity in critical piping systems, with a primary focus on the GTAW, GMAW, and SMAW processes. By consolidating and analyzing findings from more than 137 peer-reviewed studies, the review presents a comparative framework that directly links parameter behaviors, weld morphology, microstructural evolution, and mechanical performance with the operational requirements of ASME B31.3.

Several distinct scientific contributions emerge from this work:

• Process specific parameter sensitivities were systematically identified, showing that GTAW is dominated by shielding-gas composition and arc energy stability, GMAW by wire-feed dynamics and metal-transfer behaviors, and SMAW by electrode selection and groove geometry. These distinctions provide practical guidance for selecting a suitable welding process under different fabrication and field conditions.
• Quantitative trends reported in the literature indicate measurable improvement in weld performance, including tensile strength improvements of up to 10.5%, HAZ width reductions exceeding 30%, and improved bead uniformity and hardness when optimized parameter sets such as pulse-current control, Taguchi-based tuning, and data driven predictive approaches are applied.
• The review clarifies the mechanistic role of heat input, demonstrating how current, voltage, and travel speed jointly influence grain coarsening, phase balance, sensitization behavior, and residual-stress development. These effects are directly linked to long-term reliability under high-pressure and high-temperature service conditions.
• A comparative assessment across defect modes, environmental sensitivity, skill requirements, productivity, and cost provides a practical decision support perspective, linking reported research outcomes with real-world fabrication and field welding constraints.
• The analysis reinforces the connection between parameter control and ASME B31.3 compliance, particularly with respect to limits on root height, internal reinforcement, and non-destructive examination acceptance.

Despite extensive progress in weld-bead optimization, a persistent gap remains in the systematic control of internal root protrusion. Excessive root height continues to pose challenges in flow-sensitive piping due to its influence on turbulence, erosion, corrosion, and code compliance. Achieving consistent root height below 2 mm, especially in automated GTAW, remains insufficiently addressed in the current literature.

To advance this field, future research should explore smart welding systems based on adaptive algorithms, sensor fusion, arc diagnostics, and digital twin models that enable real-time control of root formation and stability. These technologies represent the most promising route toward eliminating internal discontinuities, ensuring repeatable weld morphology, and achieving next-generation weld quality aligned with ASME B31.3 and other high-integrity industrial standards.

Overall, the findings presented here consolidate existing knowledge while highlighting actionable gaps supporting the advancement of more reliable, precision-controlled, defect-resistant, and code-compliant welding practices essential for modern pipeline systems.