Methods for classifying spot welding processes: a comparative study of performance

Authors:
Eija Haapalainen;Perttu Laurinen;Heli Junno;Lauri Tuovinen;Juha Röning
Affiliations:
Intelligent Systems Group, Department of Electrical and Information Engineering, University of Oulu, Finland;Intelligent Systems Group, Department of Electrical and Information Engineering, University of Oulu, Finland;Intelligent Systems Group, Department of Electrical and Information Engineering, University of Oulu, Finland;Intelligent Systems Group, Department of Electrical and Information Engineering, University of Oulu, Finland;Intelligent Systems Group, Department of Electrical and Information Engineering, University of Oulu, Finland
Venue:
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Year:
2005

Citing 3
Cited 3

Self-organizing maps

Self-organizing maps
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Neural and statistical classifiers-taxonomy and two case studies

IEEE Transactions on Neural Networks

Entertainment modeling through physiology in physical play

International Journal of Human-Computer Studies
Preference learning for cognitive modeling: a case study on entertainment preferences

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Genetic search feature selection for affective modeling: a case study on reported preferences

Proceedings of the 3rd international workshop on Affective interaction in natural environments

Quantified Score

Hi-index	0.00

Visualization

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 set - classifier 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.