Manufacturing yield improvement by clustering

Authors:
M. A. Karim;S. Halgamuge;A. J. R. Smith;A. L. Hsu
Affiliations:
Department of Mechanical and Manufacturing Engineering, University of Melbourne, VIC, Australia;Department of Mechanical and Manufacturing Engineering, University of Melbourne, VIC, Australia;Department of Mechanical and Manufacturing Engineering, University of Melbourne, VIC, Australia;Department of Mechanical and Manufacturing Engineering, University of Melbourne, VIC, Australia
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

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Abstract

Dealing with product yield and quality in manufacturing industries is getting more difficult due to the increasing volume and complexity of data and quicker time to market expectations. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large databases. Growing self-organizing map (GSOM) is established as an efficient unsupervised datamining algorithm. In this study some modifications to the original GSOM are proposed for manufacturing yield improvement by clustering. These modifications include introduction of a clustering quality measure to evaluate the performance of the programme in separating good and faulty products and a filtering index to reduce noise from the dataset. Results show that the proposed method is able to effectively differentiate good and faulty products. It will help engineers construct the knowledge base to predict product quality automatically from collected data and provide insights for yield improvement.