Development of Defect Detection AI Model for Wire + Arc Additive Manufacturing Using High Dynamic Range Images

Abstract

Wire + arc additive manufacturing (WAAM) utilizes a welding arc as a heat source and a metal wire as a feedstock. In recent years, WAAM has attracted significant attention in the manufacturing industry owing to its advantages: (1) high deposition rate, (2) low system setup cost, (3) wide diversity of wire materials, and (4) sustainability for constructing large-sized metal structures. However, owing to the complexity of arc welding in WAAM, more research efforts are required to improve its process repeatability and advance part qualification. This study proposes a methodology to detect defects of the arch welding process in WAAM using images acquired by a high dynamic range camera. The gathered images are preprocessed to emphasize features and used for an artificial intelligence model to classify normal and abnormal statuses of arc welding in WAAM. Owing to the shortage of image datasets for defects, transfer learning technology is adopted. In addition, to understand and check the basis of the model’s feature learning, a gradient-weighted class activation mapping algorithm is applied to select a model that has the correct judgment criteria. Experimental results show that the detection accuracy of the metal transfer region-of-interest (RoI) reached 99%, whereas that of the weld-pool and bead RoI was 96%.

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

“…So far, a large number of outlier detection methods have been introduced for data analysis in WAAM [4], [12], [17], [33]. For instance, a non-contact in-situ 3D laser profilometer inspection system has been presented in [12] to monitor the visual surface defects.…”

“…In [4], a convolutional neural network-based method has been proposed for realtime anomaly detection in WAAM. In [17], an image-based approach has been presented for defect detection in WAAM.…”

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

“…With the rapid development of artificial intelligence technology, deep learning algorithms are also applied to the welding and WAAM process, particularly for prediction and optimization of process parameters (He et al , 2021a; Lu et al , 2021; Xia et al , 2022a), prediction of microstructure and property (Li et al , 2018; Qi et al , 2019; Xia et al , 2022b), geometric deviation control (Wang et al , 2021; Xiong et al , 2021; Xiong and Zhang, 2022), welding defect detection and quality assessment (Cheepu, 2023; Cho et al , 2022; He et al , 2021b; Lee et al , 2021).…”

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

“…Recent investigations have established associations between the data obtained from the manufacturing process and the quality of the fabricated layers, employing methodologies for monitoring control parameters [20]. Additionally, the implementation of intelligent sensing systems has been central in monitoring various process responses [8,[21][22][23][24][25][26][27][28][29]. Moreover, efforts have been made to assess the stability of the electric arc by analyzing metallic transfer modes during the Wire Arc Additive Manufacturing (WAAM) process [22,23].…”