Wire Arc Additive Manufacturing Process Research Articles

Numerical simulation and experimental investigation of temperature distribution during the wire arc additive manufacturing (WAAM) process

Numerical simulation and experimental investigation of temperature distribution during the wire arc additive manufacturing (WAAM) process

Investigating the Forming Characteristics of 316 Stainless Steel Fabricated through Cold Metal Transfer (CMT) Wire and Arc Additive Manufacturing

Wire and arc additive manufacturing (WAAM), recognized for its capability to fabricate large-scale, complex parts, stands out due to its significant deposition rates and cost-effectiveness, positioning it as a forward-looking manufacturing method. In this research, we employed two welding currents to produce samples of 316 austenitic stainless steel utilizing the Cold Metal Transfer wire arc additive manufacturing process (CMT-WAAM). This study initially evaluated the maximum allowable arc travel speed (MAWFS) and the formation characteristics of the deposition bead, considering deposition currents that vary between 100 A and175 A in both CMT and CMT pulse(CMT+P) modes. Thereafter, the effect of the CMT+P mode arc on the microstructure evolution was analyzed using the EBSD technique. The findings indicate that the arc travel speed and deposition current significantly affect the deposition bead’s dimensions. Specifically, an increase in travel speed or a reduction in current results in reduced bead width and height. Moreover, the employment of the CMT+P arc mode led to a reduction in the average grain size in the mid-section of the sample fabricated by CMT arc and wire additive manufacturing, from 13.426 μm to 9.429 μm. Therefore, the components of 316 stainless steel produced through the CMT+P-WAAM method are considered fit for industrial applications.

Multi-response optimisation of wire-arc additive manufacturing process parameters for AISI 4130 steel during remanufacturing process

Wire-arc additive manufacturing (WAAM) has emerged as a critical tool for remanufacturing industrial components. A limited understanding of this technique for quality product manufacturing has hindered its utilisation for industrial applications. This study reports on the optimisation of WAAM process parameters for AISI 4130 steel towards remanufacturing of high-quality products for industrial applications. AISI 4130 steel was selected for this study due to its high strength-to-weight ratio, excellent weldability, and suitability for the WAAM process. Taguchi’s Grey Relational Analysis (GRA) used four factors and three levels in the multiple response optimisation process. The study considered process parameters voltage, current, travel speed and gas flow in the gas metal arc welding (GMAW)-based WAAM technique. Analysis of Variance (ANOVA) results show that voltage, travel speed and gas flow significantly affect material deposition. Voltage had the highest significance (31.61%) compared to other parameters. The optimised process parameters were found to be: voltage –23 V, current –100 A, travel speed −350 mm min−1, and gas flow −10 L min−1. These parameters resulted in tensile residual stresses of 25 ± 74 MPa, microhardness of 171.4 ± 12.2 HV0.3, and a relative density of 98.21%. The microstructural analysis reveals the existence of predominant ferritic and pearlitic colonies. This is due to compounded thermal stresses during the deposition process and alloy composition resulting in tailored microstructure and mechanical properties. The study provides some insights into the WAAM remanufacturing process for producing highly quality industrial components.

In situ process monitoring of multi-layer deposition in wire arc additive manufacturing (WAAM) process with acoustic data analysis and machine learning

In situ process monitoring of multi-layer deposition in wire arc additive manufacturing (WAAM) process with acoustic data analysis and machine learning

Manipulating nanostructure during wire arc additive manufacturing defeats hydrogen embrittlement

Wire arc additive manufacturing (WAAM) leads to a unique nano/microstructure. The combination of multiscale experimental and numerical analyses firstly shows that the intrinsic heat treatment during WAAM leads to an unusual depletion of Mg near the grain boundaries. During the WAAM process, a high dislocation density leads to formation of sub-grains and high-angle grain boundaries (HAGBs). It is revealed that the uniformly distributed micropores formed during the WAAM are strong hydrogen traps. The reduction in Mg content at the HAGBs, an increase in total length of HAGBs and hydrogen trapping by micropores help to defeat intergranular hydrogen embrittlement by WAAM.

Microstructural heredity of Mg-Y-Nd-Al alloys prepared by WAAM

This study investigated the microstructure of Mg-4Y-3Nd-1.5Al alloys refined with Al and processed via wire arc additive manufacturing (WAAM). The size of the Al2RE particles in the initial wire significantly affects the grain size during WAAM. Larger Al2RE particles in the wire are resistant to dissolution, resulting in fewer reprecipitated Al2RE particles and, consequently, larger grains. Therefore, precise control of the Al2RE particle size as small as possible in the initial wire is essential for optimizing the refinement effect of magnesium-rare (Mg-RE) alloys in the WAAM process. This research is essential for advancing the development of Mg-RE alloys tailored for WAAM applications.

Benchmarking the quality improvement strategies of wire arc additive manufacturing process using fuzzy QFD approach

Purpose The purpose of this paper is to improve the productivity and quality of the wire arc additive manufacturing process by benchmarking the strategies from the selected six strategies, namely, heat treatment process, inter pass cooling process, inter pass cold rolling process, peening process, friction stir processing and oscillation process. Design/methodology/approach To overcome the lack of certainty associated with correlations and relationships in quality functional deployment, fuzzy numbers have been integrated with the quality functional deployment framework. Twenty performance measures have been identified from the literature under five groups, namely, mechanical properties, physical properties, geometrical properties, cost and material properties. Using house of quality weights are allocated to performance measures and groups, relationships are established between performance measures and strategies, and correlations are assigned between strategies. Finally, for each strategy, relative importance, score and crisp values are calculated. Findings Inter pass cold rolling process strategy is computed with the highest crisp value of 15.80 which is followed by peening process, heat treatment process, friction stir processing, inter pass cooling process,] and oscillation process strategy. Originality/value To the best of the authors’ knowledge, there has been no research in the literature that analyzes the strategies to improve the quality and productivity of the wire arc additive manufacturing process.

Microstructural evolution and mechanisms affecting the mechanical properties of wire arc additively manufactured Al-Zn-Mg-Cu alloy reinforced with high-entropy alloy particles

The mechanical properties of Al-Zn-Mg-Cu alloys produced via wire arc additive manufacturing (WAAM) are often weakened by the presence of continuous and coarse precipitated phases, which significantly limits their practical applications. In this study, high-entropy alloy (HEA) particles were incorporated into Al-Zn-Mg-Cu alloy during the WAAM process, which inhibits the continuity of precipitated phase, and the mechanism of microstructure evolution and macroscopic mechanical properties optimization of the components are explored. The results show that HEA particles promote grain refinement, and disrupt the formation of continuous precipitated phase along the grain boundary by generating plenty of fine spot-like precipitated phases, thus inhibiting grain boundary segregation. The spot-like precipitated phases are connected via a slender second-phase-band. The primary precipitates include the metastable η', stable η, and T phases, and the diffusion of solute atoms forms the Al3Ni and Al3(Ni, Cu)2 phases. The tensile strength increases from 247.4±5.9 MPa to 326.2±19.7 MPa in the horizontal direction and from 273.0±13.7 MPa to 335.4±11.1 MPa in the vertical direction, which correspond to increases of 31.9 % and 22.9 % respectively. The enhancement of mechanical properties is mainly attributed to Hall-Petch, Orowan, load-transfer, solid-solution and dislocation strengthening mechanisms. Only slight variation occurred in the elongation, the increased number of fine spot-like precipitated phases and grain boundaries hinder the crack propagation, while the increased number of pores facilitates the crack propagation, and these effects almost balance each other out in competition. These findings are expected to provide new insights into the microstructure optimization of WAAM components, thereby meeting more practical applications.

On the fabrication of finer grain structure through using finer wire diameter for wire arc additive manufacturing of AZ31B magnesium alloy

Magnesium (Mg) alloys are the lightest structural metallic material, but it is hard to be applied to various industrial fields due to the crystallographic nature that is composed with hexagonal closed packed (HCP). Wire arc additive manufacturing (WAAM) could be a key to fabricating Mg parts with large sizes and complex designs. It has numerous advantages such as high material efficiency, short-delivery time by faster manufacturing speed, easy to modify, etc. Yet, most of the research mentioned the grain coarsening and degradation in mechanical properties when fabricating WAAM products using Mg alloy wires. To solve the problems, a solution was suggested with supposition that the lessen the deposit metal size a little by changing to the finer wire diameter can reduce the actual heat input and heat accumulation during the WAAM process. In this research, the WAAM of AZ31B has been conducted using a wire diameter of 1.0 mm rather using the 1.2 mm that is used in common. It was found that the finer (about 90 %) and equiaxed microstructure was composing the WAAM product made of wire diameter of 1.0 mm, and it results in about two times higher mechanical strength.

Modelling the weld cladding process to predict weld clad position and shape error

Wire arc additive manufacturing (WAAM) is one of the most productive metal additive manufacturing methods. One of its most promising applications holds in the manufacturing of difficult-to-cut materials where production costs can be reduced with minimizing the time of machining and total tool costs. To develop a correct WAAM, technological processes for manufacturing complex-shaped components welding torch path corrections and welding power corrections have to be made especially in critical sections such as corners and sharp edges. A predictive mathematical model of the material cladding during the WAAM process has been developed for the purposes of generating an optimal toolpath of the WAAM clads. This predictive mathematical model is simplified to reflect the important physical phenomena in the weld pool but also to optimize computing time. In this paper, the principle of the mathematical model is described, and its functionality is verified by the welding experiments with five different welding power settings. For the initial calibration of the model parameters single straight-line weld clads with 5 different welding power settings (wire feeds) ranging from 5.0 to 8.6 m/min were investigated. 3D scans of these welded samples are used for the verification. With the calibrated simulation model, it was possible to predict the precise shape with a maximum deviation circa 0.20 mm. The start portions of the weld clads seem more complex having the deviation circa 0.30 mm. These are valuable results as the WAAM technology is generally considered to be reasonably rough.

A New Particle Swarm Optimization Algorithm for Outlier Detection: Industrial Data Clustering in Wire Arc Additive Manufacturing

In this paper, a novel outlier detection method is proposed for industrial data analysis based on the fuzzy C-means (FCM) algorithm. An adaptive switching randomly perturbed particle swarm optimization algorithm (ASRPPSO) is put forward to optimize the initial cluster centroids of the FCM algorithm. The superiority of the proposed ASRPPSO is demonstrated over five existing PSO algorithms on a series of benchmark functions. To illustrate its application potential, the proposed ASRPPSO-based FCM algorithm is exploited in the outlier detection problem for analyzing the real-world industrial data collected from a wire arc additive manufacturing pilot line in Sweden. Experimental results demonstrate that the proposed ASRPPSO-based FCM algorithm outperforms the standard FCM algorithm in detecting outliers of real-world industrial data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Electric arc (which is governed by the current and arc voltage) plays a significant role in monitoring the operating status of the wire arc additive manufacturing (WAAM) process. The nominal periodic current and voltage may occasionally change abruptly due to anomalies (such as arc instability, unstable metal transfer, geometrical deviations, and surface contaminations), which would affect the quality of the fabricated component. This paper focuses on detecting possible anomalies by analyzing the current and voltage during the WAAM process. A novel clustering-based outlier detection method is proposed for anomaly detection where abnormal and normal instances are categorized into two separate clusters. A new particle swarm optimization algorithm is put forward to optimize the initial cluster centroid so as to improve the detection accuracy. The proposed outlier detection method is applied to real-world data collected from a WAAM pilot line for detecting abnormal instances. Experimental results demonstrate the effectiveness of the proposed outlier detection method. The proposed outlier detection method can be applied to other industrial applications including electrical engineering, mechanical engineering and medical engineering. In the future, we aim to develop an online outlier detection system based on the proposed method for real-time for anomaly detection and defect prediction.

Parametric influence and effect of cooling channel on bead geometry profiles of ER70S-6 manufactured using wire arc additive manufacturing

This study investigated the complex relationship between key process parameters and their effects on weld bead geometrical profile in wire arc additive manufacturing (WAAM) of ER70S-6 low-carbon steel for part production. Specifically, the influence of wire feed rate (WFR), traverse speed (TS), and voltage (U) on weld bead height (WBH), weld bead width (WBW), and weld bead penetration (WBP) was meticulously analyzed. Research methodology integrated the Taguchi technique, analysis of variance (ANOVA), regression modeling, and gray relational analysis (GRA) to investigate and optimize process parameters. Results revealed distinct patterns, with increasing WFR leading to proportional increases in WBH, while elevated TS or U results in reduced WBH due to their impact on arc behavior and heat input. Conversely, WFR and U exhibit notable effects on WBW and WBP, underscoring the importance of optimizing process parameters for desired weld bead geometries. To tackle multi-objective optimization, GRA efficiently determines optimal process parameters for WBH, WBW, and WBP: WFR at 6 m/min, TS at 4 mm/sec, and U at 24 V. Furthermore, the inclusion of a cooling channel beneath the substrate plate significantly influences weld bead geometry, enhancing WBH and WBW while impacting WBP. The cooling channel’s ability to dissipate heat promotes faster solidification, ultimately improving weld bead quality and integrity. These findings offer valuable comprehensions for improving WAAM processes and enhancing the efficiency and quality of part fabrication.

Influence of deposition path strategy on residual stress and deformation in weaving wire-arc additive manufacturing of disc parts

Deposition pattern and path-planning deposition strategy have significant impacts on the deformation and residual stress of components during a wire-arc additive manufacturing (WAAM) process. This work comparatively addressed the influence of weaving and non-weaving deposition patterns on the distributions of residual stress and warping deformation during a single-bead-on-plate WAAM process. Further, four path strategies with different deposition sequences (in-out and out-in) and deposition directions (alternate and consistent) were designed for a weaving WAAM process of disc part, and their impacts on temperature history, warping deformation, and residual stress were comparatively analyzed through thermal-mechanical coupling finite element (FE) method. Simulation and experiment results manifest that, compared to the non-weaving pattern, the weaving deposition pattern results in more uniform deformation and lower residual stress on the deposited plate due to wider and shallower molten pool. Comparison results for the four deposition path strategies in the weaving WAAM process of disc part suggest that the out-in deposition sequence exhibits faster heat dissipation and uniform temperature field during the cooling stage, resulting in reduced warping deformation and residual stress on the workpiece. Moreover, uniform deformation is observed across path strategies employing alternate deposition direction. The out-in alternate deposition path strategy was identified as the optimal choice for the weaving WAAM process of disc part. Finally, physical experiments validated the simulated warping deformation of disc parts under different deposition path strategies. This work contributes to the design and optimization of weaving WAAM process for disc-shaped components.

Deep learning assisted fabrication of metallic components using the robotic wire arc additive manufacturing

PurposeBecause of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy.Design/methodology/approachInstead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point.FindingsThe mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively.Originality/valueThe fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.

Double pulsed current adopted in local dry underwater WAAM to simultaneously enhanced strength and ductility of 308 L multi-layer component

The double pulsed current waveform modulation was applied to address the severe challenges of greatly declined strength and ductility caused by continuous heat accumulation and water cooling in local dry underwater wire arc additive manufacturing (LDU-WAAM) process. The microstructures and mechanical performance of 308 L stainless steel multi-layer components prepared by LDU-WAAM using single pulsed waveform (SPW) and double pulsed waveform (DPW) were comprehensively investigated in this work. The results demonstrate that the microstructures of LDU-WAAM parts were both composed of abundant columnar crystals and few equiaxed crystals. Extremely directional columnar crystals dominated in each deposited layer centre prepared by SPW, while the weakened growth tendency of columnar crystal and uniform microstructure were achieved by DPW. DPW exhibited outstanding stirring action in LDU-WAAM process, which was conducive to the refined grain, improved liquidity of deposited metal, increased ferrite content, dislocation density, and austenite deformed twinning content compared with the SPW multi-layer component. Hence, the average microhardness, ultimate tensile strength, and elongation of DPW deposited layers were improved to 204.7 HV, 659.2 MPa, and 24.9 %, respectively, which enhanced by 10.1 %, 12.5 %, and 37.5 % than that of SPW sample. The research results provide a new technique optimization method to simultaneous enhance the strength and ductility of LDU-WAAM process and promote the landing application of LDU-WAAM in underwater on-site repair.

Evolutions of microstructure and mechanical property of 308L stainless steel repaired by the local dry underwater wire arc additive manufacturing

Local dry underwater wire arc additive manufacturing process (LDU-WAAM) was innovatively proposed to repair the damaged large underwater structures. A thorough investigation was performed to explore the microstructure and mechanical properties of 308L stainless steel multi-layer components prepared by LDU-WAAM technique. The results demonstrate that the microstructure of all components was composed of a significant amount of austenite and a minor amount of ferrite. Subjected to the repeated action of local arc heating and rapid water cooling, the multi-layer components were deposited into a stratified structure, each layer was mainly composed plenty of directional columnar crystals, and the adjacent layers were separated by a small number of equiaxed crystals. With the increase of deposited layer counts, the heat transfer paths from deposited metal to the substrate were gradually lengthened, which increased average grain size, LAGBs proportion, dislocation density, and austenite deformed twinning content of the multi-layer sample, also decreased its ferrite content. Hence, the microstructure of LDU-WAAM multi-layer component varied significantly from the bottom to the top, which led to the deteriorated mechanical properties along the building direction. In the 3-layer component, the ultimate tensile strengths of the first, second, and third layers were 554 MPa, 532 MPa, and 519 MPa, respectively. And the elongations of that were 25.3%, 23.2%, and 19.5%, respectively. The research results verify the feasibility of local dry underwater wire arc additive manufacturing process and provide a new process method for underwater on-site repair.

Bead geometry prediction and optimization for corner structures in directed energy deposition using machine learning

Wire Arc Additive Manufacturing as a Directed Energy Deposition technology has disruptive potential for modern manufacturing. The technology comes with the flexibility and material efficiency of additive manufacturing processes while mitigating the disadvantages through high material deposition rates and high energy efficiency. However, the prevalence of the technology is inhibited by its geometrical inaccuracy and the large induced residual stresses. This work tackles the former problem by capturing the process parameter-geometry relationship using Machine Learning. To do so, multiple mild steel welding beads with varying shape features like corner angles are printed using a Metal Inert Gas welding machine attached to an industrial robot. The cross-sectional profile of the printed beads is measured using a point laser sensor and correlated through a Multilayer Perceptron to input features such as travel speed, wire feed speed, and shape features. By incorporating varying corner angles, a holistic model, not limited to geometry prediction of straight beads only, is trained. Using the model, excess material at the inner angle of corners determined by the overlapping regions of the two adjacent beads can be predicted. By generating a database of possible bead shapes an inverse algorithm that suggests welding parameter combinations that yield a smooth bead shape at the corner is created, thus enabling improvement of the overall accuracy of the Wire Arc Additive Manufacturing process.

Effects of Contaminations on Electric Arc Behavior and Occurrence of Defects in Wire Arc Additive Manufacturing of 316L-Si Stainless Steel

Additive Manufacturing is a manufacturing process that consists of obtaining a three-dimensional object from the deposition of material layer by layer, unlike conventional subtractive manufacturing methods. Wire Arc Additive Manufacturing stands out for its high productivity among the Additive Manufacturing technologies for manufacturing metal parts. On the other hand, the excessive heat input promotes increased residual stress levels and the occurrence of defects, such as pores, voids, a lack of fusion, and delamination. These defects result in abnormalities during the process, such as disturbances in electrical responses. Therefore, process monitoring and the detection of defects and failures in manufactured items are of fundamental importance to ensure product quality and certify the high productivity characteristic of this process. Thus, this work aimed to characterize the effects of different contaminations on the electric arc behavior of the Wire Arc Additive Manufacturing process and the occurrence of microscopic defects in thin walls manufactured by this process. To investigate the presence of defects in the metal preforms, experimental conditions were used to promote the appearance of defects, such as the insertion of contaminants. To accomplish the electric arc behavior analysis, voltage and current temporal data were represented through histograms and cyclograms, and the arc stability was assessed based on the Vilarinho index for a short circuit. Effectively, the introduction of contaminants caused electric arc disturbances that led to the appearance of manufacturing defects, such as inclusions and porosities, observed through metallographic characterization. The results confirm that the introduction of contaminations could be identified early in the Wire Arc Additive Manufacturing process through electric arc data analysis.

An experimental and numerical study on the influence of interlayer time interval in wire-arc additive manufacturing process

Wire arc additive manufacturing is the derivative of the metal AM process based on arc welding and a preferred technology for manufacturing large metallic components with medium to low complexity. The major problem with the WAAM process arises due to severe heat accumulation within the built structure and repeated heating-cooling cycles during layer-by-layer material deposition. Therefore, the interlayer time interval influences the microstructure and mechanical properties of the components. This paper has characterized the impact of interlayer cooling time on the microstructure and mechanical properties of SS-316L build. The local thermal cycle causes a non-uniform cooling rate, which alters the structure of the grains. The grain size decreases with increased interlayer time interval due to a faster cooling rate brought on by the low inter-pass temperature. A wider and smaller wall was deposited with less time interval. X-ray diffraction analysis confirms the formation of austenite and ferrite phases in all WAAM components with a slight difference in intensity. The wall constructed with a maximum time interval has a maximum value of average Vickers micro-hardness 244.00 HV0.5 and possesses the least coefficient of friction due to its improved property by the formation of fine grains. The maximum volume of materials was worn out from the wall deposited with lesser cooling time. The designed numerical model predicts the peak temperature and heat accumulation within the built structure which further relates to the morphological and mechanical properties of printed parts.

Optimization of process parameters of cold metal transfer welding-based wire arc additive manufacturing of super Duplex stainless steel using response surface methodology

The parameter selection is essential to achieving the desired bead geometry and minimizing the various defects such as discontinuous weld beads, cracks, porosity, and waviness during the fabrication of wire arc additive manufacturing (WAAM) samples. The purpose of this study is to examine the effect of three input process parameters (current, welding speed, and gas flow rate) at three distinct levels on the properties (weld bead width, bead height, and dilution) of samples fabricated using super Duplex 2507 stainless steel through the cold metal transfer (CMT)–WAAM process using response surface methodology (RSM). To create a design of experiment involving three process parameters, a central composite design (CCD) matrix was utilized, and adequacy was checked by ANOVA analysis. The maximum values for weld bead width and bead height were 6.57 mm and 3.43 mm, respectively; the minimum dilution observed was 31.30%. The predicted optimal input parameters were 190.46 A current, 8.94 mm/s welding speed, and 15 l/min shielding gas flow rate. The results indicated that current was the most influential factor in determining the multiple responses, followed by welding speed and gas flow rate. The microstructures were characterized by optical microscopy, and results indicated that the microstructure of weld bead region consisted of ferrite and austenite. The microhardness of the CMT-based WAAM fabricated samples was also evaluated. This study holds significant potential for the fabrication of stainless-steel additive manufacturing products using a CMT-based arc welding process.