Article ID Journal Published Year Pages File Type
442835 Journal of Computational Design and Engineering 2016 11 Pages PDF
Abstract

•This work uses innovative methods in e-quality with the application of SVM.•Many potential benefits of e-quality control have been presented and instantiated.•The classifier equations are built on the data obtained from the experiments.•A detailed analysis is presented for six different case studies.•The results indicate the robustness of proposed SVM classification.

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.

Related Topics
Physical Sciences and Engineering Computer Science Computer Graphics and Computer-Aided Design
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