Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (2): 338-344.doi: 10.13229/j.cnki.jdxbgxb20211147

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Risk analysis of machining center failure mode based on multi⁃attribute group decision making

Gui-xiang SHEN1,2(),Jun ZHENG1,2,Ying-zhi ZHANG1,2(),Jie SONG1,2,Zhe-wen LI1,2   

  1. 1.Key Laboratory of CNC Equipment Reliability，Ministry of Education，Jilin University，Changchun 130022，China
    2.College of Mechanical and Aerospace Engineering，Jilin University，Changchun 130022，China
  • Received:2021-10-30 Online:2022-02-01 Published:2022-02-17
  • Contact: Ying-zhi ZHANG E-mail:shengx@jlu.edu.cn;zhangyz@jlu.edu.cn

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Abstract:

Aiming at the problems existing in the traditional FMEA of CNC machine tools， such as the unreasonable distribution of expert weight and risk factor weight， and the insufficient robustness of the calculation model of risk coefficient（RPN）， an improved FMEA method based on multi-attribute group decision-making is proposed. Firstly， interval number is introduced to represent the risk factor of machine tool failure mode； Secondly， considering the subjective and objective weights of experts， according to the consistency principle， the comprehensive weights of experts are calculated， and the weighted average operator（WAA） is used to determine the comprehensive evaluation matrix of fault mode； Thirdly， the interval number entropy method is used to determine the weight of risk factors， and the improved risk priority number （IRPN） calculation model is used to obtain the failure mode risk value； Finally， combined with interval number distance measure， the optimal and worst interval number improved TOPSIS method is used to rank the risk of failure mode. Taking a machining center as an example， the rationality and effectiveness of the method are verified.

Key words: machine tool, failure mode and effect analysis, multi-attribute group decision-making, improved risk priority number, technique for order preference by similarity to an ideal solution

CLC Number: 

  • TG659

Fig.1

Flow chart of improved FMEA method for multi-attribute group decision making"

Fig.2

Iterative process of expert comprehensive weight"

Fig.3

Expert weight iteration diagram"

Fig.4

Deviation degree between experts and groups"

Fig.5

Deviation among experts"

Table 1

Calculation results of closeness"

故障OPRESRIRPNFi
H1［5.4928，6.1592］［6.5194，7.4086］［5.9176，6.6743］0.5966
H2［3.4982，4.3731］［4.3769，5.0072］［3.8563，4.6384］0.0000
H3［8.2417，8.4395］［7.1791，7.9644］［7.7616，8.2294］1.0000
H4［3.7259，4.1042］［6.4459，6.7387］［4.7286，5.0917］0.0507
H5［3.8337，4.2242］［6.5900，7.2762］［4.8519，5.3507］0.0849

H6

H7

H8

H9

H10

H11

H12

H13

H14

［3.7532，3.9995］

［4.1898，4.3029］

［3.8079，3.8709］

［5.4130，6.1102］

［4.6526，5.0433］

［3.6416，4.6526］

［3.5768，3.8921］

［4.5566，5.1622］

［4.5014，5.0735］

［6.3354，6.9485］

［4.8352，5.6408］

［6.8116，7.1112］

［5.8572，6.1702］

［5.1228，5.8277］

［4.5910，5.1108］

［6.1377，6.7498］

［6.5826，7.1726］

［7.0718，7.5394］

［4.7127，5.0850］

［4.4591，4.8404］

［4.9033，5.0426］

［5.6018，6.1363］

［4.8515，5.3705］

［4.0276，4.8465］

［4.5233，4.9447］

［5.3469，5.9558］

［5.4784，6.0269］

0.0488

0.0200

0.0645

0.3791

0.0868

0.0024

0.0271

0.2677

0.3148

Fig.6

Comparison of sorting results"

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