Online evaluation method of resistance spot welding quality based on locally linear embedding algorithm

Abstract

To address issues of low efficiency, poor feedback timeliness, and unsuitability for fast-paced, high-volume manufacturing of the traditional quality inspection methods of resistance spot welding, an online evaluation method of resistance spot welding quality based on a locally linear embedding algorithm is studied for mild steel resistance spot welding to achieve cost reduction and efficiency improvement. During welding tests, voltage and current were simultaneously collected to calculate the welding power signal. We study the variation pattern of the dynamic power curve. The dynamic power signal was subjected to locally linear embedding and manual feature extraction. The collected features were then used as input to build random forest models and CatBoost models for the online weld quality evaluation, respectively. The results show that the classification models with the feature volumes constructed by locally linear embedding as input have higher assessment accuracy than manually extracted features. The locally linear embedding method can effectively eliminate the subjective influence brought by manual extraction and has better reliability. The CatBoost model based on the locally linear embedding method using the welding power signal can quickly and effectively achieve online quality assessment of mild steel spot welding, providing a further breakthrough in spot welding quality evaluation technology.