Online qualitative nugget classification by using a linear vector quantization neural network for resistance spot welding

Abstract

Real-time estimation of weld quality from process data is one of the key objectives in current weld control systems for resistance spot-welding processes. This task can be alleviated if the weld controller is equipped with a voltage sensor in the secondary circuit. Replacing the goal of quantifying the weld quality in terms of button size by the more modest objective of indirect estimation of the class of the weld, e.g., satisfactory (acceptable, “normal” button size), unsatisfactory (undersized, “cold” welds), and defects (“expulsion”), further improves the feasibility of the mission of indirect estimation of the weld quality. This paper proposes an algorithmic framework based on a linear vector quantization (LVQ) neural network for estimation of the button size class based on a small number of dynamic resistance patterns for cold, normal, and expulsion welds that are collected during the stabilization process. Nugget quality classification by using an LVQ network was tested on two types of controllers; medium-frequency direct current (MFDC) with constant current controller and alternating current (AC) with constant heat controller. In order to reduce the dimensionality of the input data vector, different sets of features are extracted from the dynamic resistance profile and are compared by using power of the test criteria. The results from all of these investigations are very promising and are reported here in detail.