Review of data mining applications for quality assessment in manufacturing industry: support vector machines

¹ LCFC, Arts et Métiers Paris Tech, HESAM, ENIM Metz, France
² Université de Lorraine, Ile du Saulcy, Metz, 57045, France

^⋆ Correspondence: hamideh.rostami@ensam.eu

Received: 26 May 2015
Accepted: 8 September 2015

Abstract

In many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in 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 be used 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 better for a particular type of dataset. 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 deal with QA problems. This review provides a comprehensive analysis of the literature from various points of view as DM concepts, 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.