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Classification of weld defects based on the analytical hierarchy process and Dempster–Shafer evidence theory

Author

Listed:
  • Hongquan Jiang

    (Xi’an Jiaotong University)

  • Rongxi Wang

    (Xi’an Jiaotong University
    Xi’an Jiaotong University)

  • Zhiyong Gao

    (Xi’an Jiaotong University)

  • Jianmin Gao

    (Xi’an Jiaotong University)

  • Hongye Wang

    (Xi’an Jiaotong University)

Abstract

Weld quality management is currently one of the most concerning issues in the manufacturing industry. In this paper, a novel method is proposed for weld defect classification based on the analytical hierarchy process (AHP) and Dempster–Shafer (DS) evidence theory. First, to overcome the problem of traditional DS methods, which weigh every feature equally in classification, a method is proposed based on AHP to calculate the weight of features (WF) of a weld defect, which can then be utilized in classification. Then, an improved method based on DS evidence theory is presented to improve the accuracy of classification, which includes calculation of the standard value of features based on frequency histograms analysis and an improved Dempster’s rule for combination based on WF. A case study on the classification of steam turbine weld defects is provided to illustrate and evaluate the proposed techniques. The results show that the proposed method increases the correct recognition rate of classification with limited samples, making DS evidence theory applicable to weld defect classification.

Suggested Citation

  • Hongquan Jiang & Rongxi Wang & Zhiyong Gao & Jianmin Gao & Hongye Wang, 2019. "Classification of weld defects based on the analytical hierarchy process and Dempster–Shafer evidence theory," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 2013-2024, April.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1369-4
    DOI: 10.1007/s10845-017-1369-4
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    References listed on IDEAS

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    1. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
    2. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
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    Cited by:

    1. Fatih Yiğit & Şakir Esnaf, 2021. "A new Fuzzy C-Means and AHP-based three-phased approach for multiple criteria ABC inventory classification," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1517-1528, August.
    2. Lu Yang & Hongquan Jiang, 2021. "Weld defect classification in radiographic images using unified deep neural network with multi-level features," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 459-469, February.
    3. Zelin Zhi & Hongquan Jiang & Deyan Yang & Jianmin Gao & Quansheng Wang & Xiaoqiao Wang & Jingren Wang & Yongxiang Wu, 2023. "An end-to-end welding defect detection approach based on titanium alloy time-of-flight diffraction images," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1895-1909, April.

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