Abstract.Resistance spot welding is an important and widely used method for joining metal objects. In this paper, various classification methods for identifying welding processes are evaluated. Using process identification, a similar process for a new welding experiment can be found among the previously run processes, and the process parameters leading to high-quality welding joints can be applied. With this approach, good welding results can be obtained right from the beginning, and the time needed for the set-up of a new process can be substantially reduced. In addition, previous quality control methods can also be used for the new process. Different classifiers are tested with several data sets consisting of statistical and geometrical features extracted from current and voltage signals recorded during welding. The best feature setclassifier combination for the data used in this study is selected. Finally, it is concluded that welding processes can be identified almost perfectly by certain features.
Resistance spot welding is used to join two or more metal objects together, and the technique is in widespread use in, for example, the automotive and electrical industries. This paper discusses both the identification of different spot welding processes and the process initialization parameters leading to highquality welding joints. In this research, self-organizing maps (SOMs) were used, and optimal features for the training parameters were sought. According to the results, processes can be classified by specific features. When introducing new data to trained SOMs, the welding operator can visually identify similar processes. After process identification, the most similar process is retrieved and a self-organizing map is trained for this specific process. The initialization parameters leading to successful welds in that process can thus be identified, which means that the manufacturers can use them to initialize their welding machines.
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