Dongguk University
Assembly Line
Using AI to Detect Faulty Crimps
Crimp force monitoring (CFM) has long been the standard for fault detection in wire assemblies. The technique can reliably detect many defects, including wrong strip length, missing strands, wrong wire cross section, wrong terminal, inconsistent terminal material, insulation in the crimp, wrong insertion depth, and wrong crimp height.
We propose a fault detection system that employs AI with regional selective data scaling (RSDS). RSDS generates synthetic abnormal data from reference data by performing upscaling or downscaling on specific regions of the data. This allows the fault detection system to efficiently train an AI model with a dataset comprised exclusively of normal operational data and still achieve high accuracy in detecting faults.
In this study, a multilayer perceptron (MLP) classification model was trained exclusively on normal data and was able to effectively distinguish between normal and abnormal conditions. To validate the system, 15 unique raw datasets from a real-world wire harness manufacturing facility were collected and tested with four anomaly detection algorithms: Isolation Forest, one-class autoencoders, k-means, and Histogram-Based Outlier Score (HBOS).