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

Hongquan Jiang (), Rongxi Wang (), Zhiyong Gao (), Jianmin Gao () and Hongye Wang ()
Additional contact information
Hongquan Jiang: Xi’an Jiaotong University
Rongxi Wang: Xi’an Jiaotong University
Zhiyong Gao: Xi’an Jiaotong University
Jianmin Gao: Xi’an Jiaotong University
Hongye Wang: Xi’an Jiaotong University

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 4, No 31, 2013-2024

Abstract: 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.

Keywords: Radiographic testing; Classification; Analytical hierarchy process (AHP); Dempster–Shafer (DS) Evidence theory (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-017-1369-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1369-4

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-017-1369-4

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().