Generating Synthetic Training Images to Detect Split Defects in Stamped Components

Detecting rare and costly defects, such as necks and splits in sheet metal stamping, remains challenging for deep learning models due to low failure rates entailing few available samples to train on. Synthetic images provide a simulated alternative; however, the two main current approaches have limitations for generating split defect images. Image synthesis-based models generate implausible training data, while physics-based models are computationally expensive and lack the diversity required. To address this, we present a novel method combining the advantages of physics-based simulation with synthetic-based defect generation. The method first generates deformed 3-D geometry through finite element simulation with plausible split locations determined using a forming limit curve. Subsequently, the fine details of captured real splits are mapped to the identified locations to generate realistic defect features. Our results show that training a deep neural network with the addition of synthetic images improves the performance significantly.