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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Acknowledgements
This work is supported in part by the National Natural Science Foundation of China (51775112, 71771033), the Postdoctoral Science Foundation of China (2016M602459), and the Research Program of Higher Education of Guangdong (2016KZDXM054). We also extend special thanks to the editor/reviewers for their valuable comments in improving the quality of this paper.
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Bai, Y., Sun, Z., Zeng, B. et al. A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction. J Intell Manuf 30, 2245–2256 (2019). https://doi.org/10.1007/s10845-017-1388-1
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DOI: https://doi.org/10.1007/s10845-017-1388-1