Abstract
Process identification in the field of resistance spot welding can be used to improve welding quality and to speed up the set-up of a new welding process. Previously, good classification results of welding processes have been obtained using a feature set consisting of $54$ features extracted from current and voltage signals recorded during welding. In this study, the usability of the individual features is evaluated and various feature selection methods are tested to find an optimal feature subset to be used in classification. Ways are sought to further improve classification accuracy by discarding features containing less classification-relevant information. The use of a small feature set is profitable in that it facilitates both feature extraction and classification. It is discovered that the classification of welding processes can be performed using a substantially reduced feature set. In addition, careful selection of the features used also improves classification accuracy. In conclusion, selection of the feature subset to be used in classification notably improves the performance of the spot welding process identification system.
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TWI: World centre for materials joining technology. Resistance Spot Welding, www document (2004) Retrieved September 29, 2005, from \url{www.twi.co.uk/j32k/protected/band_3/kssaw001.html}.
Anderson, T.: Radiographic and ultrasonic weld inspection: establishing weld integrity without destroying the component. Practical Welding Today, December 13 (2001)
Cho, Y., Rhee, S.: Primary circuit dynamic resistance monitoring and its application to quality estimation during resistance spot welding. Welding Journal 81 (2002) 104–111
Haapalainen, E., Laurinen, P., Junno, H., Tuovinen, L., Röning, J.: Methods for classifying spot welding processes: a comparative study of performance. In: Proc. 18th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (2005) 412–421
Stoppiglia, H., Dreyfus, G., Dubois, R., Oussar, Y.: Ranking a random feature for variable and feature selection. Journal of Machine Learning Research 3 (2003) 1399–1414
Dash, M., Liu, H.: Feature selection for classification. International Journal of Intelligent Data Analysis 1 (1997) 131–156
Kudo, M., Sklansky, J.: Comparison of algorithms that select features for pattern classifiers. Pattern Recognition 33 (2000) 25–41
Jain, A., Zongker, D.: Feature selection: evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997) 153–158
Reinsch, C.: Smoothing by spline functions, II. Numerische Matematik 16 (1971) 451–454
Junno, H., Laurinen, P., Haapalainen, E., Tuovinen, L., Röning, J., Zettel, D., Sampaio, D., Link, N., Peschl, M.: Resistance spot welding process identification and initialization based on self-organizing maps. In: Proc. First International Conference on Informatics in Control, Automation and Robotics, Volume 1 (2004) 296–299
Junno, H., Laurinen, P., Tuovinen, L., Röning, J.: Studying the quality of resistance spot welding joints using self-organising maps. In: Proc. 4th International ICSC Symposium on Engineering of Intelligent Systems (2004)
Junno, H., Laurinen, P., Haapalainen, E., Tuovinen, L., Röning, J.: Resistance spot welding process identification using an extended knn method. In: Proc. IEEE International Symposium on Industrial Electronics, Volume 1 (2005) 7–12
Devijver, P., Kittler, J.: Pattern Recognition – A Statistical Approach. Prentice-Hall, Englewood Cliffs, NJ (1982)
Pudil, P., Ferri, F., Novovičová, J., Kittler, J.: Floating search methods for feature selection with nonmonotonic criterion functions. In: Proc. 12th International Conference on Pattern Recognition (1994) 279–283
Stearns, S.: On selecting features for pattern classifiers. In: Proc. Third International Conference on Pattern Recognition (1976) 71–75
Narendra, P., Fukunaga, K.: A branch and bound algorithm for feature selection. IEEE Transactions on Computers C-26 (1977) 917–922
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Haapalainen, E., Laurinen, P., Junno, H., Tuovinen, L., Röning, J. (2008). Feature Selection for Identification of Spot Welding Processes. In: Cetto, J.A., Ferrier, JL., Costa dias Pereira, J., Filipe, J. (eds) Informatics in Control Automation and Robotics. Lecture Notes Electrical Engineering, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79142-3_7
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DOI: https://doi.org/10.1007/978-3-540-79142-3_7
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