Application of the Extended $k$ nn Method to Resistance Spot Welding Process Identification and the Benefits of Process Information

Resistance spot welding is used to join two or more metal objects, and the technique is widely used in, for example, the automotive and electrical industries. This paper introduces the use of the k-nearest-neighbor (knn) method to identify similar welding processes. The two main benefits achieved from knowing the most similar process are the following: 1) The time needed for the setup of a new process can be substantially reduced by restoring the process parameters leading to high-quality joints, and 2) the quality of new welding spots can be predicted and improved using the stored information of a similar process. In this paper, the basic knn method was found to be inadequate, and an extension of the knn method, which is called similarity measure, was developed. The similarity measure provides information of how similar the new process is by using the distance to the knns. Based on the results, processes can be classified, and the similarity measure proved to be a valuable addition to the existing methodology. Furthermore, process information can provide a major benefit to welding industry.