Quality Monitoring for Micro Resistance Spot Welding with Class-Imbalanced Data Based on Anomaly Detection

• Main Authors: Jiaquan Zeng, Biao Cao, Ran Tian
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Applied Sciences
• Subjects: micro resistance spot welding; quality monitoring; class-imbalanced data; anomaly detection; isolation forest
• Online Access: https://www.mdpi.com/2076-3417/10/12/4204

Micro resistance spot welding (MRSW) is an important technology widely used in electronics manufacturing for micro component joining. For the joining of micro enameled wire, quality control is heavily dependent on manual inspection till now. In this paper, a quality monitoring approach based on isolation forest (iForest) is proposed to identify abnormal welds and normal welds. Electrode voltage and welding current of over 110,000 spot welds were collected from a production line. The dynamic resistance and heat input were calculated for all welds and used for feature extraction. A class imbalance problem existed in the collected dataset because abnormal welds were far fewer than normal welds. The anomaly detection model based on iForest was established for the imbalanced data classification after comparison with other methods such as one-class (support vector machine) SVM and local outlier factor. Test results show that the similarity of dynamic resistance profile and heat input compared with the previous ten welds are valid features for detecting a part of the abnormal welds. The iForest model is effective for distinguishing incomplete fusion welds from normal welds with high efficiency. It can assist in the on-line quality monitoring of enameled wire welding process in production.