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

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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.

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Acknowledgements

The authors sincerely thank the referees for their helpful suggestions and comments, which greatly improved the quality of the paper. This research was supported by the National Natural Science Foundation of China (Grant No. 51375375).

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Authors and Affiliations

  1. State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an, China

    Hongquan Jiang, Rongxi Wang, Zhiyong Gao, Jianmin Gao & Hongye Wang

  2. Institute of Manufacturing Systems and Quality Engineering in the Department of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China

    Rongxi Wang

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  1. Hongquan Jiang
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Correspondence to Rongxi Wang.

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Jiang, H., Wang, R., Gao, Z. et al. Classification of weld defects based on the analytical hierarchy process and Dempster–Shafer evidence theory. J Intell Manuf 30, 2013–2024 (2019). https://doi.org/10.1007/s10845-017-1369-4

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  • DOI: https://doi.org/10.1007/s10845-017-1369-4

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