Human-Process-Inspired Automated Layer Segmentation for Quality Assessment in Wire-Arc Additive Manufacturing

Large-scale metal additive manufacturing has become increasingly popular in aerospace and petroleum industries alike for sustainable fabrication of thin-shelled structural components. For example, wire-arc additive manufacturing (WAAM) offers high-deposition rates on large printing areas by robot-assisted welding of thick layers of material. However, WAAM technologies suffer from significant layer displacement and resultant part-scale distortion due to unstable high temperature deposition processes and lack of economically viable support structures. Therefore, geometric accuracy qualification at layer level is critical to process optimization and control. However, layer quality assessment relies on layer identification from large point clouds. Manual layer segmentation is experience-dependent and time-consuming due to high surface roughness, excessive layer remelting, and severe out-of-plane layer displacement. To enable automated layer segmentation for quality assessment, we computationally model a human operator’s intuition utilized in the process of finding layer boundaries, that is, locating nearby regions with a large number of possible boundary points, and finetuning boundaries by learning boundaries functions. In our proposed approach, geometrical features of the boundaries between printed layers are exploited to identify candidate boundary points. Cooperative multi-learning-agents efficiently process the large point clouds to locate sets of nearby regions with a high density of boundary points. Learning agents then sample promising boundary points. Gaussian process regression is employed to fine-tune layer boundaries through learning mean boundary functions and their uncertainties from the sampled points. Simulation studies demonstrate the accuracy and robustness of the procedure under severe surface roughness conditions. WAAM experimental studies illustrate the applicability of the methodology in practice.