Search Results (136)

Wire arc additive manufacturing (WAAM) has emerged as a promising technology for producing large-scale metal components due to its high deposition efficiency, low material cost, and design flexibility. However, the widespread industrial adoption of WAAM is hindered by challenges in geometric accuracy, process stability, and defect control, which are closely related to two critical aspects: trajectory planning and real-time weld seam tracking. This review provides a comprehensive and critical analysis of recent advances in both fields, with an emphasis on their interconnection rather than treating them as separate research streams. Unlike existing reviews that primarily summarize path planning algorithms or image processing techniques in isolation, this paper explicitly examines the integration challenges and synergistic potential between offline trajectory optimization and online vision-based monitoring. Key topics include adaptive path strategies for sharp corners and intersections, interlayer filling methods to mitigate heat accumulation and residual stress, as well as passive and active visual sensing technologies for molten pool characterization and defect detection. The review further identifies a persistent gap in closed-loop systems that combine real-time image feedback with dynamic path replanning. Based on the analysis of representative studies, current limitations are discussed and future research directions are proposed, including the development of digital twins, multi-modal data fusion, and reinforcement learning-based adaptive control. This review offers a distinct perspective aimed at advancing intelligent, high-precision WAAM systems for complex metal components. Full article

Additive manufacturing (AM), also known as three-dimensional (3D) printing, has emerged as a revolutionary digital near-net-shape manufacturing technology, offering innovative solutions for the design and fabrication of complex, high-performance structures and equipment. This paper reviews the recent advancements and applications of metal AM technologies in the marine sector. Firstly, the principles and characteristics of three most widely adopted metal AM processes in this field are introduced: laser powder bed fusion (L-PBF), directed energy deposition (DED), and wire arc additive manufacturing (WAAM). Subsequently, the application status of metal AM is summarized in four key marine sectors: propulsion systems, underwater vehicle housings and structures, hull structures and shipboard equipment and components, as well as marine equipment repair and emergency support. Building on this, the major challenges for metal AM applications in the marine environment are further discussed, including the fabrication of large-scale components, standardization of materials and processes, integration of smart manufacturing and digital technologies, and sustainability and circular manufacturing. Finally, future trends are projected toward higher efficiency, intelligence, and environmental sustainability. It is indicated that metal AM will fundamentally reshape the manufacturing mode of marine equipment and support its high-performance, low-cost, intelligent and rapid-response development. Full article

This study quantitatively evaluates the impact of Wire Arc Additive Manufacturing (WAAM) process parameters on the environmental performance of components produced in Invar 36 alloy. An experimental campaign involving 49 parameter sets was carried out by varying wire feed speed, welding voltage, and welding speed. For each condition, electrical signals, shielding gas consumption, and wire usage were measured and converted into parameter-resolved Life Cycle Inventory (LCI) data. A cradle-to-gate Life Cycle Assessment (LCA) was implemented in SimaPro 9.6 using the European CML-IA baseline v3.10 midpoint method, adopting 1 kg of as-built deposited Invar 36 as the functional unit. Results show that feedstock production represents the dominant hotspot (8.68 kg CO₂-eq/kg), while the WAAM stage contributes between 1.13 and 4.12 kg CO₂-eq/kg, leading to a total impact ranging from 9.81 to 12.80 kg CO₂-eq/kg. As a result, this study demonstrates that process parameter selection strongly influences environmental performance. Indeed, Specific Energy Consumption (SEC) ranges from 0.44 to 1.95 kWh/kg, while argon consumption varies between 0.26 and 1.51 kg/kg of deposited material. By analysing the results and excluding unstable or manufacturing-infeasible deposition regimes, the optimal trade-off between process stability and environmental impact is achieved at approximately WFS = 7 m/min, V = 20 V, and WS = 6.5 mm/s. Beyond quantifying the environmental hotspots of Invar 36 WAAM, this study provides a dedicated, parameter-resolved cradle-to-gate LCA based on experimentally measured foreground data collected across 49 process parameter combinations. By combining environmental assessment with feasibility screening of the investigated deposition regimes, the work identifies not only environmentally favourable conditions, but also parameter regions that are technologically viable for WAAM processing of Invar 36. The resulting dataset provides a benchmark foundation for future sustainability-oriented process optimisation and decision support in WAAM. Full article

Wire Arc Additive Manufacturing (WAAM) has gained attention as a metal additive manufacturing process producing complex large-scale components with high deposition rates and lower costs. Cold Metal Transfer (CMT) offers reduced heat input and enhanced control of metal transfer, making it suitable for aluminium. This review analyses CMT-based WAAM with a focus on Al–Si alloys, providing a synthesis for this material system and establishing a structured comparison of representative studies on process fundamentals, arc mode variants, and key processing parameters. The influence of electrical and kinematic parameters and thermal management on process and geometrical stability, microstructural evolution, defect formation, and mechanical behaviour is discussed. Process behaviour is governed by the temporal distribution of heat input within the CMT cycle and thermal history. Control of heat input can reduce porosity, microstructural heterogeneity, and geometric instability, while advanced CMT modes can improve process stability and material efficiency under appropriate process configurations. Mechanical performance depends on the interaction between process parameters, microstructure, and defects, leading to variability and anisotropy. Despite progress, challenges related to process repeatability, narrow processing windows, defect susceptibility, and predictive capability remain. Future research should focus on parameter optimization, integrated modelling, real-time control, and WAAM-specific alloys to enable reliable industrial implementation. Full article

In modern production, Wire Arc Additive Manufacturing (WAAM) is becoming an essential technology for manufacturing complex components. However, the complexity of planning such processes constrains their widespread use in production cycles. Using various numerical simulation approaches allows for the investigation of resulting geometries with respect to process parameters, reducing the need for experiment-based process planning. Similar to various subtractive processes, there is increased interest in integrating simulation approaches into digital twin applications for planning and optimization of WAAM processes. This requires dynamic geometry mapping and simulation time comparable to the process duration. In this paper, a numerical simulation employing a Dexel-based geometry representation and a model for single-bead geometry parameter prediction is investigated as a vital alternative to Finite Element Method (FEM)-based simulations. The focus lies on the accuracy of the simulated components with respect to the simulation settings, the time needed for it to complete, and the degree of compliance between the simulated and produced multi-layer structures. Using optimized simulation settings achieves an accuracy loss of under 7% due to geometry discretization, with a simulation time that is approximately 37% faster than the process duration. The simulated components closely correspond to the experimental ones in terms of width and height, with a volumetric similarity ranging from 63.3% to 88.8%. Full article

Wire and arc additive manufacturing is a promising technology for fabricating large and complex metallic components. Wire arc methods, like MIG and MAG, use an electric arc to melt and deposit metal wire layer-by-layer. The improvement of the surface depends on the multi-bead overlapping model. However, the high quality of multi-layer deposits is reduced by structural irregularities, such as geometric defects, poor fusion, and reduced mechanical properties of the weld bead. The analysis of a single weld bead that solidifies on a base material can be carried out to improve the geometry of the microstructure, to improve the mechanical properties, and to understand the relationship between welding parameters and the bead dimensions. In the present study, current metal welding technologies and strategies in wire-arc additive manufacturing were discussed, and different weld bead geometries using BÖHLER SG2 solid wire were realized, varying the robot’s trajectory length and welding speed. The computational models are proposed to create a dependence between the controllable welding input parameters and resulting geometrical weld bead outputs (width, height, length, and radius) for prediction and optimization. These models, using techniques such as support vector machines and artificial neural networks, can be a good tool for controlling quality by understanding these input–output relationships. However, the SVM has revealed a superior performance based on metrics for the nonlinear and intricate relationships between the geometrical weld beads and welding parameters. Full article

This work aims to evaluate the economic and environmental performance of hybrid additive manufacturing (HAM) chains with metal forming operations in comparison with conventional manufacturing approaches. The approach integrates processes such as Wire-Arc Directed Energy Deposition (DED-Arc), machining, and incremental sheet forming to combine material deposition, shaping, and finishing within a single processing chain. To support this, a process-based cost model (PBCM) was developed to estimate production costs by linking process parameters with technological and operational variables and implementing computer-assisted modeling of the processing chain for identification of the production costs and corresponding key cost drivers. In parallel, a cradle-to-gate Life Cycle Assessment (LCA) was performed to evaluate environmental impacts across the stages of the HAM chain. The results indicate that direct labor, material, and machine usage are the primary cost drivers in the HAM chain. Compared to conventional chains of machining from solid or die casting, HAM achieves high reductions in production cost, from 67.8% to 84.5%, and in environmental impact of up to one order of magnitude, due to lower material consumption and independence from dedicated tooling. Overall, this work provides an integrated framework for the economic and environmental assessment of HAM, laying the foundation for future industrial implementation. Full article

This study reviews the application of wire arc additive manufacturing (WAAM) technology in maritime engineering and investigates an experimentally driven analytical approach for prediction of thermal distributions based on the Rosenthal solution. Two ER70S-6 low-carbon steel WAAM cylinders were fabricated using gas metal arc welding (GMAW) and plasma arc welding (PAW) processes, with interlayer temperatures of 453 °C and 250 °C, respectively. Accurately measuring the temperature field to tailor the microstructure has long been a challenge. The results indicated a significant deviation between the analytical predictions and the experimental data. To address this discrepancy, a hybrid approach combining analytical and experimental results was implemented. Time intervals between layers, extracted from the experimental data, were incorporated into the Rosenthal equation to improve the accuracy of temperature field predictions. The microstructure at the bottom, middle, and top regions of the WAAM components was examined using optical microscopy. Tensile testing and Vickers microhardness measurements were conducted to evaluate mechanical properties. Scanning electron microscopy (SEM) was used to analyze fracture surfaces and identify fracture modes. The results were consistent with those reported for other ER70S-6 cylindrical WAAM components. This work highlights limitations of the Rosenthal solution and emphasizes the need for thermal models in WAAM applications. Full article

Additive manufacturing technologies for metals are developing rapidly. Among them, wire arc additive manufacturing (WAAM) has become widespread due to its accessibility. However, parts produced using WAAM require surface post-processing; therefore, hybrid technologies have emerged that combine additive and subtractive processes within a single compact manufacturing complex. Such systems make it possible to organize single-piece and small-batch production, including for the repair and restoration of equipment in remote areas. For this purpose, hybrid equipment must be lightweight, compact for transportation, provide sufficient workspace, and be capable of folding for transport. This paper proposes the concept of a multifunctional metal 3D printer based on hybrid technology, where WAAM is used for printing, and mechanical post-processing is applied to obtain finished parts. To ensure both rigidity and low mass, a 3-UPU parallel manipulator and a worktable with two rotational degrees of freedom are employed, enabling five-axis printing and machining. The printer housing is foldable for convenient transportation. The kinematics of the proposed 3D printer are investigated as an integrated system. Forward and inverse kinematics problems are solved, the velocities and accelerations of the moving platform center are calculated, singular configurations are analyzed, and the workspace of the printer is determined. Full article

Topology optimisation and lattice design constitute key enablers in the transition towards high-performance and resource-efficient engineering, particularly within the framework of additive manufacturing and welding-based deposition processes. The increasing integration of arc-based technologies, such as Wire Arc Additive Manufacturing, has strengthened the relevance of these methodologies by enabling the fabrication of large-scale, structurally efficient components with controlled material distribution and mechanical performance. These design strategies provide unique opportunities to achieve lightweight structures, functionally graded behaviour, and tailored properties beyond the limitations imposed by conventional manufacturing and joining techniques. The growing demand for functionally efficient components in sectors such as aerospace, biomedical, and automotive engineering continues to drive the adoption of these approaches, where both material efficiency and structural integrity under welding-induced thermal effects are critical. This chapter introduces the fundamentals of topology optimisation and functionally graded lattice architectures, describes their integration into advanced design and manufacturing workflows, including welding-based additive processes, and presents selected case studies that demonstrate their practical impact. Finally, emerging strategies based on generative design and artificial intelligence are discussed as key drivers for the automated and process-aware optimisation of future additively manufactured and welded structures. Full article

Directed Energy Deposition (DED), as a core branch of additive manufacturing, encompasses two typical processes: laser directed energy deposition (LDED) and wire and arc additive manufacturing (WAAM), which are widely used in manufacturing aerospace engine blades and core components of high-end equipment. In recent years, with the increasing adoption of deep learning (DL) technologies, the research focus in DED has gradually shifted from traditional “process parameter optimization” to “AI-driven process optimization” and “online real-time monitoring”. Given the complex and distinct influence mechanisms of key parameters (such as laser power/arc current, scanning/travel speed) on melt pool behavior and forming quality in the two processes, the introduction of artificial intelligence to address both common and specific issues has become particularly necessary. This review systematically summarizes the application of DL techniques in both types of DED processes. It begins by outlining DL frameworks, such as artificial neural networks (ANNs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and reinforcement learning (RL), and their compatibility with DED data. Subsequently, it compares the application scenarios, monitoring accuracy, and applicability of AI in DED process monitoring across multiple dimensions, including process parameters, optical, thermal fields, acoustic signals, and multi-sensor fusion. The review further explores the potential and value of DL in closed-loop parameter adjustment and reinforcement learning control. Finally, it addresses current bottlenecks such as data quality and model interpretability, and outlines future research directions, aiming to provide theoretical and engineering references for the intelligent upgrade and quality improvement of both DED processes. Full article

This work focuses on wire arc additive manufacturing for the rapid prototyping of shell-type parts such as sealed containers/capsules required in the manufacturing of metal components using hot isostatic pressing (HIP) of powder. The selected material was AISI 316L. The automatic generation step of robot trajectories from the CAD design of the part to be manufactured was addressed first. The mechanical and metallurgical properties of WAAM samples were then evaluated. Finally, a hollow cylindrical capsule manufactured by WAAM was used for the HIP sintering of powder to demonstrate the relevance of the hybrid technology. The main results are as follows: 1. The Ultimate Tensile Strength (UTS) of AISI 316L WAAM samples was measured be-19 tween 540 MPa (longitudinal direction) and 600 MPa (transverse direction). 2. The as-manufactured WAAM parts present a residual (δ) ferrite content of 5–7%. 3. HIP processing permitted to reset a fully austenitic structure within the WAAM wall/shell. 4. The grain size was found to be coarser in the WAAM walls and finer in the core of the part (made of sintered powder). Finally, the suggested hybrid process may become an alternative technology for the manufacture of medium-size metal components in the nuclear industry. Full article

Wire Arc Additive Manufacturing (WAAM), also known as Wire Arc Directed Energy Deposition, is used for fabricating large metallic components with high deposition rates. However, the process often leads to residual stress, distortion, defects, undesirable microstructure, and inconsistent bead geometry. These challenges necessitate reliable in-situ monitoring for process understanding, quality assurance, and control. While several reviews exist on in-situ monitoring in other additive manufacturing processes, systematic coverage of sensing methods specifically tailored for WAAM remains limited. This review fills that gap by providing a comprehensive analysis of existing in-situ monitoring approaches in WAAM, including thermal, optical, acoustic, electrical, force, and geometric sensing. It compares the relative maturity and applicability of each technique, highlights the challenges posed by arc light, spatter, and large melt pool dynamics, and discusses recent advances in real-time defect detection and control, process monitoring, microstructure and property prediction, and minimization of residual stress and distortion. Apart from providing a synthesis of the existing literature, the review also provides research needs, including the standardization of monitoring methodologies, the development of scalable sensing systems, integration of advanced AI-driven data analytics, coupling of real-time monitoring with multi-physics modeling, exploration of quantum sensing, and the transition of current research from laboratory demonstrations to industrial-scale WAAM implementation. Full article

This study employs a multilevel modeling approach to describe the deformation of specimens made from austenitic Wire Arc Additive Manufactured (WAAM) steel 308LSi. Two WAAM processing modes were investigated: (1) the Cold Metal Transfer (CMT) method and (2) Cold Metal Transfer combined with interlayer deformation strengthening (hammer peening/forging). Test specimens were cut from the deposited walls at 0° and 90° relative to the deposition direction. The grain and dendritic structures of the specimens were analyzed using optical stereomicroscopy. A statistical multilevel model has been developed, accounting for the features of the grain-dendritic and defect structures under various technological deposition modes. Parameter identification and model verification were conducted based on experimental data from uniaxial tensile tests of 308LSi steel specimens. The maximum deviation of the numerical results from the experimental data during the identification stage under uniaxial tensile loading did not exceed 3%, and during the verification stage it did not exceed 10%; the overall mean deviation did not exceed 1% for the identification stage and 2% for the verification stage. The model effectively captured the anisotropic mechanical behavior of WAAM-processed samples. The maximum calculated yield strength 360 MPa was obtained for specimens cut at an angle of 45°, while the minimum value 331 MPa was observed for vertically oriented specimens. Specimens subjected to interlayer forging (hammer peening) exhibited isotropic material properties. Explicit multilevel modeling, incorporating the presence of MnO oxide inclusions located within the austenite matrix, was performed. The results showed good correlation with experimental data and confirmed the localization of fatigue cracks at the phase boundary-matrix-oxide interface. Full article

In the context of Industry 4.0, new methods of manufacturing, monitoring, and data generation related to industrial processes have emerged. Over the last decade, a new method of part manufacturing that has been revolutionizing the industry is Additive Manufacturing, which comes in various forms, including the more traditional Fusion Deposition Modeling (FDM) and the more innovative ones, such as Laser Metal Deposition (LMD) and Wire Arc Additive Manufacturing (WAAM). New technologies related to monitoring these processes are also emerging, such as Cyber-Physical Systems (CPSs) or Digital Twins (DTs), which can be used to enable Artificial Intelligence (AI)-powered analysis of generated big data. However, few works have dealt with a comprehensive data analysis, based on Digital Twin systems, to study quality levels of manufactured parts using 3D models. With this background in mind, this current project uses a Digital Twin-enabled dataflow to constitute a basis for a proposed data analysis pipeline. The pipeline consists of analyzing metal AM-manufactured parts’ surface roughness quality levels by the application of a Deep Neural Network (DNN) analytical model and enabling the assessment and tuning of deposition parameters by comparing AM-built models’ 3D representation, obtained by photogrammetry scanning, with the positional data acquired during the deposition process and stored in a cloud database. Stored and analyzed data may be further used to refine the manufacturing of parts, calibration of sensors and refining of the DT model. Also, this work presents a comprehensive study on experiments carried out using the CW-GTAW (Cold Wire Gas Tungsten Arc Welding) process as the means of depositing metal, resulting in hollow parts whose geometries were evaluated by means of both 3D scanned data, obtained via photogrammetry, and positional/deposition process parameters obtained from the Digital Twin architecture pipeline. Finally, an adapted PointNet DNN model was used to evaluate surface roughness quality levels of point clouds into 3 classes (good, fair, and poor), obtaining an overall accuracy of 75.64% on the evaluation of real deposited metal parts. Full article