Mining association rules for the quality improvement of the production process

  • Authors:

  • Bernard Kamsu-Foguem;Fabien Rigal;FéLix Mauget

  • Affiliations:

  • Laboratory of Production Engineering (LGP), EA 1905, ENIT, INPT, University of Toulouse, 47 Avenue d'Azereix, BP 1629, 65016 Tarbes Cedex, France;Laboratory of Production Engineering (LGP), EA 1905, ENIT, INPT, University of Toulouse, 47 Avenue d'Azereix, BP 1629, 65016 Tarbes Cedex, France;Laboratory of Production Engineering (LGP), EA 1905, ENIT, INPT, University of Toulouse, 47 Avenue d'Azereix, BP 1629, 65016 Tarbes Cedex, France

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2013

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Abstract

Academics and practitioners have a common interest in the continuing development of methods and computer applications that support or perform knowledge-intensive engineering tasks. Operations management dysfunctions and lost production time are problems of enormous magnitude that impact the performance and quality of industrial systems as well as their cost of production. Association rule mining is a data mining technique used to find out useful and invaluable information from huge databases. This work develops a better conceptual base for improving the application of association rule mining methods to extract knowledge on operations and information management. The emphasis of the paper is on the improvement of the operations processes. The application example details an industrial experiment in which association rule mining is used to analyze the manufacturing process of a fully integrated provider of drilling products. The study reports some new interesting results with data mining and knowledge discovery techniques applied to a drill production process. Experiment's results on real-life data sets show that the proposed approach is useful in finding effective knowledge associated to dysfunctions causes.