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This study aims to investigate surface roughness, microstructure, and mechanical properties of overhead thin-wall structures of stainless steel(SS316L) fabricated by cold metal transfer (CMT)-based wire + arc additive manufacturing (WAAM). In the first stage, single-layer bead experiments were carried out in flat and overhead positions utilizing Box-Behnken experimental design with a range of process parameters (i.e., wire feed rate, travel speed, and weave amplitude). To study the effect of individual process parameters on the bead geometry and identify a process window, analysis of variance(ANOVA) is performed using the bead cross-section measurement data. For single layer bead experiments in flat and overhead position, out of all process parameters, the weave amplitude is the most significant parameter on bead width, whereas travel speed is most significant parameter for bead height. Based on single-layer bead experiments, process parameters for thin wall deposition were identified. In the second stage, two thin-walls were deposited with wire feed rates of 1000 and 1500 mm/min in the overhead position. The surface roughness was measured using cloud point data acquired from the coordinate measuring machine(CMM). The deposited structure with the wire feed rate of 1500 mm/min resulted in better surface quality. It was also observed that, microstructure was composed of austenite and dendritic delta ferrite. The microstructure changed as the deposition height increased. The average microhardness value was measured 183 HV and 187.4 HV for the overhead structures. Average tensile properties of the SS316L overhead structures were comparable to that of SS316L fabricated by other WAAM processes. 

Wire arc additive manufacturing is a promising additive manufacturing process because of its high deposition rate, and material diversity. However, the low quality of melted parts is a critical issue, owing to the difficulty in establishing design rules for process–structure–property–performance. Previous studies have resolved this challenge by deriving anomaly detection models for quality monitoring and have largely relied on machine learning by training melt pool image data. Acquiring sufficient data is a key to obtaining reliable models in machine learning; however, an issue arises from concerning the cost intensiveness in high-cost materials. We propose a material-adaptive anomaly detection method to detect balling defects in a target material using property-concatenated transfer learning. First, transfer learing is applied to derive convolutional neural network (CNN)-based models from a source material and transfer them to a target material, wherein data are insufficient and machine learning rarely achieves high performance. Second, material properties are concatenated on transfer learning as additional features onto image features, contrary to typical transfer learning where CNNs only extract image features. We perform experiments in a gas tungsten arc welding system with low-carbon steel (LCS), stainless steel (STS), and inconel (INC) materials. Our models achieve best classification accuracies of 82.95%, 89.47%, and 84.22% when transferring from LCS to STS, LCS to INC, and STS to INC, respectively, compared with 78.03%, 86.37%, and 73.63% obtained using typical transfer learning. The proposed method can effectively resolve the data scarcity by model transfer from sufficient datasets in low-cost materials to rare datasets in high-cost materials. Moreover, it outperforms typical transfer learning because material properties are learned as manufacturing-knowledge features, accounting for melting and hardening characteristics of materials. 

Abstract Wire arc additive manufacturing (WAAM) has gained attention as a feasible process in large-scale metal additive manufacturing due to its high deposition rate, cost efficiency, and material diversity. However, WAAM induces a degree of uncertainty in the process stability and the part quality owing to its non-equilibrium thermal cycles and layer-by-layer stacking mechanism. Anomaly detection is therefore necessary for the quality monitoring of the parts. Most relevant studies have applied machine learning to derive data-driven models that detect defects through feature and pattern learning. However, acquiring sufficient data is time- and/or resource-intensive, which introduces a challenge to applying machine learning-based anomaly detection. This study proposes a multisource transfer learning method that generates anomaly detection models for balling defect detection, thus ensuring quality monitoring in WAAM. The proposed method uses convolutional neural network models to extract sufficient image features from multisource materials, then transfers and fine-tunes the models for anomaly detection in the target material. Stepwise learning is applied to extract image features sequentially from individual source materials, and composite learning is employed to assign the optimal frozen ratio for converging transferred and present features. Experiments were performed using a gas tungsten arc welding-based WAAM process to validate the classification accuracy of the models using low-carbon steel, stainless steel, and Inconel.