Investigating the ability of deep learning to predict Welding Depth and Pore Volume in Hairpin Welding

arXiv:2312.01606v3 Announce Type: replace
Abstract: To advance quality assurance in the welding process, this study presents a robust deep learning model that enables the prediction of two critical welds Key Performance Characteristics (KPCs): welding depth and average pore volume. In the proposed approach, a comprehensive range of laser welding Key Input Characteristics (KICs) is utilized, including welding beam geometries, welding feed rates, path repetitions for weld beam geometries, and bright light weld ratios for all paths, all of which were …

abstract advance arxiv cs.lg deep learning key performance prediction process quality quality assurance robust study type