Authors:
Heli Junno
1
;
Perttu Laurinen
1
;
Eija Haapalainen
1
;
Lauri Tuovinen
1
;
Juha Röning
1
;
Dietmar Zettel
2
;
Daniel Sampaio
2
;
Norbert Link
2
and
Michael Peschl
2
Affiliations:
1
University of Oulu, Finland
;
2
Fachhochschule Karlsruhe, Institut für Innovation und Transfer, Germany
Keyword(s):
Resistance spot welding, Self-organizing maps, Process identification, Initialization parameters
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Neural Networks Based Control Systems
Abstract:
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 high-quality 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.