Quick dimensional inspection for continuous welding and assembly using machine learning-powered smart jig

Abstract

In the mass production of metal-based products such as automobiles, continuous welding and assembly processes are essential. The final product is created through multiple stages of welding, and the cumulative misalignment at each stage can lead to excessive residual stresses or dimensional defects in the product. To compensate for these issues, design modifications or significant post-processing costs have been required. Traditional dimensional inspection methods, whether manual or automated, are limited in their ability to keep pace with the speed required for mass production, as they focus on point-by-point measurements. While 3D vision-based methods offer a solution, they are often costly and primarily suited for macro-scale inspections. Here, we propose a machine learning-powered smart jig that enables precise, micro-level dimensional quality monitoring during production, without interrupting the continuous manufacturing process. This method, designed for direct integration into continuous assembly welding lines, reduces inspection time from 12 min to 2.79 s, enabling the detection of dimensional errors at the 500 mu m level. Demonstrations conducted on the production line at a commercial automobile manufacturer confirm the feasibility of this approach for comprehensive subassembly inspections during mass production. This system is expected to be highly adaptable for various manufacturing domains utilizing assembly jigs, offering transformative potential in quality inspection processes.