Review of data mining applications for quality assessment in manufacturing industry: Support Vector Machines

Hamideh Rostami (1) , Jean-Yves Dantan (1) , Lazhar Homri (1)

1 LCFC - Laboratoire de Conception Fabrication Commande

In many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in the databases. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for quality assessment (QA) in manufacturing industries. In DM, the choice of technique to use in analyzing a dataset and assessing the quality depend on the understanding of the analyst. On the other hand, with the advent of improved and efficient prediction techniques, there is a need for an analyst to know which tool performs best for a particular type of data set. Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique, namely support vector machine (SVM) to solve QA problems. The review provides a comprehensive analysis of the literature from various points of view as DM preliminaries, data preprocessing, DM applications for each quality task, SVM preliminaries, and application results. Summary tables and figures are also provided besides to the analyses. Finally, conclusions and future research directions are provided.

Mots clés: Data mining; Quality assessment; Manufacturing industry; Support vector machine

Domaines: Génie des procédés

Hamideh Rostami, Jean-Yves Dantan, Lazhar Homri. Review of data mining applications for quality assessment in manufacturing industry: Support Vector Machines. International Journal of Metrology and Quality Engineering, 2015, 6 (4), 59p. ⟨10.1051/ijmqe/2015023⟩. ⟨hal-01344715⟩