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%.

Additive manufacturing (AM) has been envisioned by many as a driving factor of the next industrial revolution. Potential benefits of AM adoption include the production of low-volume, customized, complicated parts/products, supply chain efficiencies, shortened time-to-market, and environmental sustainability. Work remains, however, for AM to reach the status of a full production-ready technology. Whereas the ability to create unique 3D geometries has been generally proven, production challenges remain, including lack of (1) data manageability through information management systems, (2) traceability to promote product producibility, process repeatability, and part-to-part reproducibility, and (3) accountability through mature certification and qualification methodologies. To address these challenges in part, this paper discusses the building of data models to support the development of validation and conformance methodologies in AM. We present an AM information map that leverages informatics to facilitate part producibility, process repeatability, and part-to-part reproducibility in an AM process. We present three separate case studies to demonstrate the importance of establishing baseline data structures and part provenance through an AM digital thread.

Additive manufacturing (AM) has emerged as a transformative technology for fabricating intricately tailored components, representing a paradigm shift in design versatility and material optimization. The fabrication of high-strength Al7XXX series alloys through this approach is attracting increasing interest due to their lightweight nature, exceptional mechanical properties, and corrosion resistance, making them essential for aerospace, automotive, and defense applications. Despite these advantages, manufacturing these alloys through AM remains challenging due to issues such as hot cracking, porosity, elemental evaporation, adhesion and bonding defects, and microstructural anisotropy. Recent studies have explored strategies to address these challenges, demonstrating successful fabrication and evaluating potential applications in advanced engineering. This review aims to report the current state of manufacturing techniques, categorizing them into solid-state and melting-based approaches while analyzing their processing mechanisms, challenges, and mechanical performance. Strategies for defect mitigation, including process optimization, post-processing treatments such as heat treatment and grain refinement, and novel alloy modifications, are critically reviewed. By consolidating recent advancements and identifying key challenges, this study establishes a foundation for future research, emphasizing defect control, microstructural stability, and industrial scalability for high-performance applications.