A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction

Yun Bai, Zhenzhong Sun, Bo Zeng, Jianyu Long, Lin Li, José Valente Oliveira and Chuan Li ()
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Yun Bai: Dongguan University of Technology
Zhenzhong Sun: Dongguan University of Technology
Bo Zeng: Dongguan University of Technology
Jianyu Long: Dongguan University of Technology
Lin Li: Dongguan University of Technology
José Valente Oliveira: Universidade do Algarve
Chuan Li: Dongguan University of Technology

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 5, No 12, 2245-2256

Abstract: Abstract Manufacturing quality prediction model, as an effective measure to monitor the quality in advance, has been developed using various data-driven techniques. However, multi-parameter in multi-stage of the modern manufacturing industry brings about the curse of dimensionality, leading to the difficulties for feature extraction, learning and quality modeling. To address this issue, three dimension reduction techniques are investigated in this paper, i.e., principal component analysis (PCA), locally linear embedding (LLE), and isometric mapping (Isomap). Specifically, the PCA is a linear dimension reduction technique, the LLE is a nonlinear reduction technique with local perspective, and the Isomap is a nonlinear reduction technique from global perspective. After getting the low-dimensional information from the PCA, the LLE, and the Isomap methods respectively, a support vector machine (SVM) is utilized for modeling. To reveal the effectiveness of the dimension reduction techniques and compare the difference of the three dimension reduction techniques, two experimental manufacturing data are collected from a competition about manufacturing quality control in Tianchi Data Lab of China. The comparison experiments indicate that the dimension reduction techniques have capacity for improving the SVM modeling performance indeed, and the Isomap–SVM model with the nonlinear global dimension reduction outperforms all the candidate models in terms of qualitative and quantitative analysis.

Keywords: PCA; LLE; Isomap; SVM; Manufacturing quality prediction (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (14)

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DOI: 10.1007/s10845-017-1388-1

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