Search Results (39)

This study presents a mathematical and kinematic modeling framework for analyzing trajectory behavior in a virtual reality (VR) welding simulator. Twenty novice participants performed repeated welding trials across three sessions, with torch trajectories recorded at 50 Hz in the task space. The proposed framework combines trial-level performance descriptors with derivative-based dynamic features, including spectral arc length (SPARC), log-normalized jerk (LNJ), and the number of velocity peaks (NVP), to characterize movement smoothness, intermittency, and longitudinal trajectory organization in a computer-simulated manual welding task. The results showed that spatial welding error decreased most clearly during the earliest stage of practice, with mean absolute lateral error declining from approximately 2.8 mm in the first trial to approximately 1.7 mm by the third trial. This early improvement was then broadly preserved across subsequent sessions. In contrast, smoothness- and fragmentation-related metrics exhibited more variable temporal patterns, indicating that improvements in task-space accuracy were not necessarily accompanied by uniform reorganization of movement dynamics. Associations between spatial error and kinematic features remained limited, suggesting that geometric task accuracy and dynamic trajectory organization represent complementary aspects of simulated manual performance. Overall, the findings show that high-frequency trajectory analysis in VR provides a useful basis for the mathematical modeling of dynamic behavior in simulated welding systems and supports the use of computer simulation for process-level investigation of manual task execution. Full article

This study investigates the feasibility of acoustic signal analysis for the assessment of weld bead quality in the shielded metal arc welding (SMAW) process. The work focuses on comparing time-domain acoustic signals and time–frequency spectrogram representations for the classification of welds as accepted or rejected according to standard welding inspection criteria. Two key acoustic descriptors, the fundamental frequency (F₀) and the harmonics-to-noise ratio (HNR), were extracted and analyzed to evaluate statistical differences between the two weld quality classes. Statistical tests, including Anderson–Darling, Levene, ANOVA, and Kruskal–Wallis (α = 0.05), revealed significant differences between accepted and rejected welds. Accepted welds exhibited a bimodal HNR distribution associated with transient arc instability at the beginning and end of the bead, whereas rejected welds showed more uniform acoustic behavior throughout the process. Subsequently, the acoustic data were represented using both audio signals and spectrograms and used as inputs for ten supervised machine learning models, including Support Vector Classifier (SVC), Logistic Regression (LR), k-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), and Naïve Bayes (NB). The results demonstrate that spectrogram-based representations significantly outperform time-domain signals, achieving accuracies of 0.95–0.96, ROC-AUC values above 0.95, and false positive and false negative rates below 6%. These findings indicate that, while scalar acoustic descriptors provide statistically significant insight into weld quality, time–frequency representations combined with machine learning enable a more robust and reliable framework for automated non-destructive evaluation, particularly in manual SMAW processes under realistic operating conditions. Full article

This study evaluates the feasibility of acoustic signal analysis using different spectrographic transformation methods as a tool for assessing the quality of welding beads produced through the Shielded Metal Arc Welding (SMAW) process. Acoustic emissions were recorded during manual welding operations under controlled experimental conditions, using E6013 electrodes on A36 carbon steel plates. From the acoustic recordings of 400 welding samples, previously classified as accepted or rejected, two fundamental acoustic descriptors were extracted: the fundamental frequency (F₀) and the harmonic-to-noise ratio (HNR). These were analysed using parametric and non-parametric metrics to evaluate their discriminative capability. In addition, multiple supervised classifiers were trained and validated using stratified eight-fold cross-validation. The proposed framework enables a systematic comparison of different signal transformations and classification models for the evaluation of SMAW welding quality. Among the evaluated models (SVC, Gradient Boosting, and Extra Trees), precision rates of 90–95% were observed using Spectral Contrast, MEL, and CQT transformations. The results demonstrate that the implementation of various acoustic signal-based models and transformations for welding inspection offers a scalable and cost-effective solution for industrial quality control. Full article

This paper examines the use of metallurgical process waste in welding electrode coatings and their impact on the quality and properties of welded joints in carbon steels. The results demonstrate that the use of metallurgical process waste in slag coatings for welding electrodes can significantly reduce the cost of the charge without degrading the welding properties of the electrodes or the quality and properties of the welded joints. The new electrode under study was fabricated using technological waste of metallurgical plants for the welding electrodes (IMAN-9). An activator additive consisting of nanostructured functional ceramics ZB-1 was introduced into the coating at an amount of 1% to increase the melting capacity of the welding electrode. This electrode is characterized by low cost, good welding properties, and ensures high-quality welded joints and beads. This paper also includes comparative tests of welded joints in low-carbon AISI 1017 steel using the IMAN-9 electrode and the ESAB E6013 electrode, which is widely used in the global market. Research has shown that IMAN-9 electrodes, compared to ESAB E6013 electrodes, offer superior performance in terms of weld metal penetration, weld metal structure, and weld joint ductility. In terms of strength, welds made by the IMAN-9 electrodes are slightly inferior to those made by the ESAB E6013 electrodes. Full article

The present study investigates the effect of welding heat input on the corrosion resistance of duplex stainless steel (DSS) overlays, with particular focus on applications in pressure vessels and clad plates. ER2209 filler metal was deposited onto SA-516 Gr. 70 carbon steel using GMAW, both manually and mechanized, with varying heat inputs. Microstructural characterization included ferrite-content measurement, macrographic analysis, and pitting-corrosion testing according to ASTM G48 Method A. The results indicate that increasing the heat input from 548 J mm⁻¹ to 2319 J mm⁻¹ significantly reduced the ferrite content from 49% to 25%, leading to a corresponding increase in weight loss from 0.55% to 2.5%. Mechanized welding exhibited better arc stability and more consistent phase distribution compared to the manual process. Although we did not detect brittle phases or intermetallic precipitates due to strict interpass temperature control, the microstructural imbalance induced by high-heat-input directly compromised the corrosion resistance. These findings advance understanding of the optimized welding parameters required to ensure the integrity of DSS coatings in aggressive environments. Full article

In this paper, the selection of the optimal base material to be used in the repair welding process is presented. The aim of the study was to determine which of the available materials has the best characteristics, based on an analysis conducted in a company engaged in construction works. Three base materials were considered in the study: ABRADUR 58, E DUR 600, and CrWC 600 electrodes. Repair welding was performed on components for a construction machinery facility using the manual metal arc welding procedure. For the selection of the optimal base material, a combined Multi-Attribute Decision-Making (MADM) approach was applied. The base materials were evaluated based on four attributes: wear track width, cost, mass loss, and hardness of welded layers. The Best–Worst Method (BWM) was used to determine the attribute weights, while the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Ranking based on the Distances And Range (RADAR) methods were applied in parallel for the ranking and selection of base materials. The analysis showed that in the considered case, the E DUR 600 electrode was the most suitable choice, which was confirmed through the application of both the TOPSIS and RADAR methods. Full article

This study develops a unified continuum damage mechanics (CDM) model for high-cycle fatigue life prediction of large manually arc-welded flange shafts manufactured from 45Mn steel (quenched and tempered) under combined bending–torsion loading. Fatigue tests revealed consistent crack initiation at the weld toe, with multiaxial loading reducing fatigue life by 35–42% compared to pure bending. The CDM parameters were calibrated against experimental data and implemented through an ABAQUS 2021 UMAT subroutine, achieving prediction errors below 5%—significantly outperforming conventional nominal and hotspot stress methods. For high-cycle fatigue conditions, a simplified CDM model neglecting plastic damage maintained engineering accuracy while improving computational efficiency by 3–5 times. The damage variable D = 0.9 was identified as a universal threshold for accelerated damage progression. These findings provide quantitative basis for multiaxial fatigue design and structural health monitoring of large welded components. Full article

This research presents a novel, low-cost optical acquisition system based on infrared imaging for real-time weld bead geometry monitoring in Gas Metal Arc Welding (GMAW). The system uniquely employs a commercial CCD camera (1000–1150 nm) with tailored filters and lenses to isolate molten pool thermal radiation while mitigating arc interference. A single camera and a mirror-based setup simultaneously capture weld bead width and reinforcement. Acquired images are processed in real time (10 ms intervals) using MATLAB R2016b algorithms for edge segmentation and geometric parameter extraction. Dimensional accuracy under different welding parameters was ensured through camera calibration modeling. Validation across 35 experimental trials (over 6000 datapoints) using laser profilometry and manual measurements showed errors below 1%. The resulting dataset successfully trained a Support Vector Machine, highlighting the system’s potential for smart manufacturing and predictive modeling. This study demonstrates the viability of high-precision, low-cost weld monitoring for enhanced real-time control and automation in welding applications. Full article

Process control and quality assurance of wire + arc additive manufacturing (WAAM) and automated welding rely heavily on in-process monitoring videos to quantify variables such as melt pool geometry, location and size of droplet transfer, arc characteristics, etc. To enable feedback control based upon this information, an automatic and robust segmentation method for monitoring of videos and images is required. However, video segmentation in WAAM and welding is challenging due to constantly fluctuating arc brightness, which varies with deposition and welding configurations. Additionally, conventional computer vision algorithms based on greyscale value and gradient lack flexibility and robustness in this scenario. Deep learning offers a promising approach to WAAM video segmentation; however, the prohibitive time and cost associated with creating a well-labelled, suitably sized dataset have hindered its widespread adoption. The emergence of large computer vision models, however, has provided new solutions. In this study a semi-automatic annotation tool for WAAM videos was developed based upon the computer vision foundation model SAM and the video object tracking model XMem. The tool can enable annotation of the video frames hundreds of times faster than traditional manual annotation methods, thus making it possible to achieve rapid quantitative analysis of WAAM and welding videos with minimal user intervention. To demonstrate the effectiveness of the tool, three cases are demonstrated: online wire position closed-loop control, droplet transfer behaviour analysis, and assembling a dataset for dedicated deep learning segmentation models. This work provides a broader perspective on how to exploit large models in WAAM and weld deposits. Full article

Gas metal arc welding (GMAW) is widely used for its productivity and ease of automation across various industries. However, certain tasks in shipbuilding and heavy industry still require manual welding, where quality depends heavily on operator skill. Defects in manual welding often necessitate costly rework, reducing productivity. Vision sensing has become essential in automated welding, capturing dynamic changes in the molten pool and arc length for real-time defect insights. Laser vision sensors are particularly valuable for their high-precision bead profile data; however, most current models require offline inspection, limiting real-time application. This study proposes a deep learning-based system for the real-time monitoring of both the molten pool and weld-bead profile during GMAW. The system integrates an optimized optical design to reduce arc light interference, enabling the continuous acquisition of both molten pool images and 3D bead profiles. Experimental results demonstrate that the molten pool classification models achieved accuracies of 99.76% with ResNet50 and 99.02% with MobileNetV4, fulfilling real-time requirements with inference times of 6.53 ms and 9.06 ms, respectively. By combining 2D and 3D data through a semantic segmentation algorithm, the system enables the accurate, real-time extraction of weld-bead geometry, offering comprehensive weld quality monitoring that satisfies the performance demands of real-time industrial applications. Full article

The main directions and fields of the application of metal nanopowders in joining technologies are considered. Based on this analysis, the purpose of this research was to determine the effect of tungsten nanopowder on the structure and properties of the deposited metal. In order to increase the efficiency of using tungsten nanopowder for modification, it is necessary to ensure the introduction of nanopowder into the low-temperature zone of the molten metal during surfacing. To study the metal, microstructural analysis was performed, and the microhardness of the deposited joint was determined. On the basis of the conducted studies, a change in the structure of the deposited metal and an increase in mechanical properties were revealed. A conclusion is made about the effect of tungsten nanopowder on the metal modification process during manual metal arc, metal inert gas, and flux-cored arc welding. Based on the conducted studies, it was found that the introduction of tungsten nanopowder into the low-temperature zone of the molten metal ensures the modification of the surfacing and induces an increase in the microhardness of the deposited metal. At the same time, grains of polyhedral morphology are formed at the surface, and the structure of oriented dendrites at the boundary of fusion with the base metal is also revealed, showing the peculiarities of the distribution of microhardness in various surfacing methods. The minimum and maximum values of microhardness depend not only on the nanopowder but also on the method of its introduction into the molten metal. Full article

As a result of the thermo-mechanical impact during welding, distortions are generated in welded structures. These distortions significantly influence the geometric and dimensional accuracy of welded structures, in many cases lowering their working characteristics and reliability. An optimal design for welded structures is a prerequisite for increased reliability and reduction in manufacturing cost, and such an optimal design can be achieved knowing the distortions in weldments. Despite the fact that pulsed metal inert gas welding and metal active gas welding have been broadly applied in the last few decades, nowadays, few manufacturers, for instance, Fronius, EWM, Redco, and Perfect Power Welders, offer such an option for manual arc welding. This work aims to determine the influence of the parameters of pulsed welding modes on distortions that are generated during manual arc welding. Two different inverter welding power sources were used, and the welding distortions were measured by 3D scanning. The results showed that the pulsed mode during manual arc welding led to a reduction in distortions compared to the conventional welding mode. The crucial part of the manual welding system proved to be the qualification and performance of the welder. Full article

As high-strength and ultra-high-strength steels are widely used in all kinds of modern welded constructions, a lot of research is carried out to investigate the mechanical properties of the weldments of these steels, but there is little information on such important characteristics as their corrosion behaviour. This research focuses on the corrosion behaviour of the weld metal of the weldments of S906QL and S700MC steels. The weld metal was tested electrochemically in a 3.5% NaCl aqueous solution via a potentiodynamic scan to determine the corrosion rate and its dependence on the welding gap. No influence of the welding gap on the corrosion rate was found, but the experimental results suggested that the corrosion rate depended on the chemical composition of the filler material and the microstructure of the weld metal. Full article

The autogenous manual gas tungsten arc welding (GTAW) process was used for cladding austenitic stainless steel 316L using a single pass with various contact tip-to-work distances (CTWDs). Immersion and electrochemical tests were used to evaluate the corrosion resistance of the welded specimens, and a microstructural analysis was conducted to investigate the chemical composition of the molten pool and the heat-affected zone of welding. The key findings of this study indicate that the corrosion resistance improved under a CTWD of 5 mm due to the optimal distribution of ferrite and a refined microstructure. Additionally, the highest hardness was observed in specimens with a CTWD of 3 mm, attributed to the increased ferrite content in the weld metal. As the CTWD increased, the ferrite fraction decreased, and the hardness also diminished. However, in the CTWD 7 mm case, the higher heat input influenced the microstructure and molten pool shape significantly through the Marangoni effect, resulting in a lower corrosion resistance. These results suggest that optimizing the CTWD can enhance the corrosion resistance of welded 316L stainless steel. Full article

The study presents the results of the research into the effect of the dynamic properties of inverter and diode power sources of welding arc power supply on the stability of melting and transfer of electrode metal into the weld pool. The principal energy parameters of the power source include the rates of rise and fall of short-circuit current, the ratio of arc burning current to short-circuit current, and other related factors. It has been demonstrated that an increase in the rate of change of these parameters within one welding mode microcycle alters the properties of heat and mass transfer, increases the frequency of electrode metal droplet transfer, reduces the size of transferred droplets in the weld pool and the duration of their stay on the electrode end under the influence of the high temperature of the welding arc, and the duration of short circuits. The increase in the mass fraction of alloying elements at their transition from the coated electrode to the weld metal is demonstrated to depend on the rate of change of the main energy parameters of one welding mode microcycle of the inverter power source in comparison with the diode rectifier. An enhancement in the structural integrity and properties of permanent joints during welding has been observed when using an inverter power source for the welding arc with high dynamic properties. Full article