Artificial Intelligence‐Assisted Repair System for Structural and Electrical Restoration Using 3D Printing

Abstract

The characteristic of material accumulation makes 3D printing competitive in remanufacturing and repairing. However, conventional repair methods require additional equipment and manual intervention to sequentially finish complicated processes such as global scanning and reverse modeling, which results in efficiency reduction and usage restriction. To address the existing shortcomings, an automatic repair system with artificial intelligence (AI) assistance is developed, which includes a semantic segmentation module, deep reinforcement learning (DRL) module, and composite printing device. The damaged part features are extracted by semantic segmentation from the captured real‐time images to establish DRL maps, where the print motion is simulated and transmitted to the printer. The results indicate that the applied bilateral segmentation network (BiSeNetV2) is 59.03% and 29.90% faster than pyramid scene parsing network (PSPNet) and DeepLabV3+ architecture with satisfying accuracy. The established DRL model based on actual printing achieves the optimization of agent learning speed and print quality. The automatic system improves the repair efficiency by 294% compared to the conventional methods, and enables both structural and electrical repair through high‐temperature polymer–metal printing. This intelligent system enables industrial robots to independently handle unexpected tasks in complex and changeable environments through interdisciplinary knowledge integration of advanced manufacturing and AI.