Journal of Computational Design and Engineering

Society for Computational Design and Engineering (한국CDE학회)
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E-quality control: A support vector machines approach

  • Tseng, Tzu-Liang (Bill) (Department of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso) ;
  • Aleti, Kalyan Reddy (Department of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso) ;
  • Hu, Zhonghua (Department of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso) ;
  • Kwon, Yongjin (James) (Department of Industrial Engineering, Ajou University)
  • Received : 2015.01.07
  • Accepted : 2015.06.15
  • Published : 2016.04.01
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Abstract

The automated part quality inspection poses many challenges to the engineers, especially when the part features to be inspected become complicated. A large quantity of part inspection at a faster rate should be relied upon computerized, automated inspection methods, which requires advanced quality control approaches. In this context, this work uses innovative methods in remote part tracking and quality control with the aid of the modern equipment and application of support vector machine (SVM) learning approach to predict the outcome of the quality control process. The classifier equations are built on the data obtained from the experiments and analyzed with different kernel functions. From the analysis, detailed outcome is presented for six different cases. The results indicate the robustness of support vector classification for the experimental data with two output classes.

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