Abstract
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.
Highlights
With the booming development of miniaturization and integration of electronic devices, micro enameled wires have been increasingly used in the manufacture of various electronic products, such as electroacoustic devices, micro coils, micro delay and chip inductor et al [1,2]
Different process signals are analyzed for feature extraction, such as dynamic resistance [11], welding demand in industry, there is a lack of investigation into quality monitoring in the micro resistance spot welding (MRSW) for micro power [12], electrode force [13], electrode displacement [14] and multiple signals [15]
The constant voltage control mode was selected for micro enameled wire joining, since it had better adaptability than constant current and constant which made the welding current vary in the welding process
Summary
With the booming development of miniaturization and integration of electronic devices, micro enameled wires have been increasingly used in the manufacture of various electronic products, such as electroacoustic devices, micro coils, micro delay and chip inductor et al [1,2]. Different process signals are analyzed for feature extraction, such as dynamic resistance [11], welding demand in industry, there is a lack of investigation into quality monitoring in the MRSW for micro power [12], electrode force [13], electrode displacement [14] and multiple signals [15]. Recent years, machine learning models have been found to be effective for predicting weld quality, Despite comprehensive studies on the weld quality of MRSW and RSW, specific applications including artificial network [16,17], decision tree [18], random forest [19] and SVM [20]. Compared with RSW or MRSW of metal sheets, MRSW of enameled development of machine learning technology provides effective decision support tools for weld wire toquality pad is quite a different physical process and has rarely been explored before. A classification approach based on isolation forest (iForest) is proposed to assist in the quality inspection
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