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Erschienen in: Journal of Intelligent Manufacturing 5/2019

05.01.2018

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

verfasst von: Yun Bai, Zhenzhong Sun, Bo Zeng, Jianyu Long, Lin Li, José Valente de Oliveira, Chuan Li

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 5/2019

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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.

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Metadaten
Titel
A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction
verfasst von
Yun Bai
Zhenzhong Sun
Bo Zeng
Jianyu Long
Lin Li
José Valente de Oliveira
Chuan Li
Publikationsdatum
05.01.2018
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 5/2019
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-017-1388-1

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