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Product quality improvement method in manufacturing process based on kernel optimisation algorithm

Author

Listed:
  • Zhe Wei
  • Yixiong Feng
  • Zhaoxi Hong
  • Rongxia Qu
  • Jianrong Tan

Abstract

Quality data in manufacture process has the features of mixed type, uneven distribution, dimension curse and data coupling. To apply the massive manufacturing quality data effectively to the quality analysis of the manufacture enterprise, the data pre-processing algorithm based on equivalence relation is employed to select the characteristic of hybrid data and preprocess data. KML-SVM (Optimised kernel-based hybrid manifold learning and support vector machines algorithm) is proposed. KML is adopted to solve the problems of manufacturing process quality data dimension curse. SVM is adopted to classify and predict low-dimensional embedded data, as well as to optimise support vector machine kernel function so that the classification accuracy can be maximised. The actual manufacturing process data of AVIC Shenyang Liming Aero-Engine Group Corporation Ltd is demonstrated to simulate and verify the proposed algorithm.

Suggested Citation

  • Zhe Wei & Yixiong Feng & Zhaoxi Hong & Rongxia Qu & Jianrong Tan, 2017. "Product quality improvement method in manufacturing process based on kernel optimisation algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5597-5608, October.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:19:p:5597-5608
    DOI: 10.1080/00207543.2017.1324223
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    Cited by:

    1. Yang Hui & Xuesong Mei & Gedong Jiang & Fei Zhao & Ziwei Ma & Tao Tao, 2022. "Assembly quality evaluation for linear axis of machine tool using data-driven modeling approach," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 753-769, March.
    2. Yang Hui & Xuesong Mei & Gedong Jiang & Fei Zhao & Pengcheng Shen, 2020. "Assembly consistency improvement of straightness error of the linear axis based on the consistency degree and GA-MSVM-I-KM," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1429-1441, August.

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