Title
Research on reliability allocation technology for NC machine tool meta‐action
Abstract
The reliability of machine tool is getting more and more attention. Reasonable reliability allocation is beneficial to improve the inherent reliability of machine tool. However, the existing reliability allocation methods for machine tool have some limitations. For example, static part is selected as the reliability allocation object of machine tool, which cannot reflect the characteristics "function realized by motion"; factors affecting reliability allocation are not considered comprehensively, weights of experts are treated as the same, and the allocation results are not optimized or the impact of time on enterprise is neglected in the optimization. To solve these problems, a new multiobjective optimization reliability allocation method for machine tool is proposed in this paper. Firstly, the latest achievements about meta-action are given, and meta-action is set as the reliability allocation object. Secondly, more reasonable and comprehensive factors affecting reliability allocation are extracted. Thirdly, expert weight coefficient is brought to reduce the subjective impact of expert scoring. Fourthly, time factor is brought to make the optimized allocation results more reasonable and accurate. Finally, a numerical control (NC) machine tool made in China is taken as an example, with a comparison on the reliability allocation results of current methods and proposed method. The results verify the applicability, rationality, and accuracy of the proposed method, which lays a foundation for the subsequent study on the quality characteristics of machine tool based on meta-action.
Year
DOI
Venue
2019
10.1002/qre.2489
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
Keywords
Field
DocType
machine tool,meta-action,optimization,reliability allocation
Econometrics,Industrial engineering,Engineering,Machine tool
Journal
Volume
Issue
ISSN
35.0
6.0
0748-8017
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Yulong Li102.03
Genbao Zhang2810.98
Yongqin Wang300.68
Xiaogang Zhang401.01
Yan Ran512.72