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Innovation fusion design of mechanical system robust design

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Abstract

In view of the reliability design-related issues, a comprehensive robust design method which considers reliability allocation is proposed. According to the failure frequency information provided by a machine tool factory, the spindle system of lathe is selected to conduct failure mode and effects analysis (FMEA). Based on FMEA nonlinear third-order correction conversion function, by considering the correlation of failure mode, failure frequency and failure severity, a reliability allocation method of tandem system by Gumbel copula function is established. The reliability allocation method combines the Kendall τ correlation failure coefficient to obtain the reliability of each subsystem and then uses the Edgeworth series method of the fourth-order moment theory and the reliability of the subsystem obtained from the reliability allocation to optimize the important part of the subsystem. Take the lathe spindle system as an example. When t=500 h and the reliability of the spindle system is 0.999, the reliability allocation results of each subsystem with and without the correlation are calculated according to the proposed method. It is found that the reliability allocation value of each subsystem is lower than when correlation is not considered. This reflects the superiority of the method of considering failure correlation. Then, considering the reliability allocation results, the reliability robust design optimization of computer numerical control machine tool  spindle is carried out, and the sensitivity analysis of the results is carried out. This method is based on the actual data of machine tool factory and comprehensively considers the integration of mechanical systems and the correlation between subsystems, and reduces the sensitivity of the variables to the maximum extent under the condition of the reliability of the subsystem. So, parts and overall system robustness are improved.

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The test data used to support the findings of this study are included within the article. Readers can obtain data supporting the research results from the test data table in the paper.

References

  1. Kai Y (1998) Robust design and reliability. Trans Nanjing Univ Aeronaut Astronaut (China) 15:9–15

    MATH  Google Scholar 

  2. Shoemaker AC, Tsui K-L, Wu CFJ (1991) Economical experimentation methods for robust design. Technometrics (USA) 33:415–427

    Article  Google Scholar 

  3. Myers RH (1999) Response surface methodology-current status and future directions. J Qual Technol (USA) 31:30–44

    Article  Google Scholar 

  4. Khattree R (1996) Robust parameter design: a response surface approach. J Qual Technol 28:187–198

    Article  Google Scholar 

  5. Pregibon D (1984) Review: P. McCullagh, J. A. Nelder, Generalized Linear Models. Ann Stat 12:1589–1596

    Article  Google Scholar 

  6. Xiaoping D, Wei C (2000) Towards a better understanding of modeling feasibility robustness in engineering design. J Mech Des 122:385–394

    Article  Google Scholar 

  7. António CC, Hoffbauer LN (2009) An approach for reliability-based robust design optimisation of angle-ply composites. Compos Struct 90:53–59. https://doi.org/10.1016/j.compstruct.2009.01.008

    Article  Google Scholar 

  8. Doh J, Yang Q, Raghavan N (2020) Reliability-based robust design optimization of polymer nanocomposites to enhance percolated electrical conductivity considering correlated input variables using multivariate distributions. Polymer 186:122060. https://doi.org/10.1016/j.polymer.2019.122060

    Article  Google Scholar 

  9. Wu H, Zheng H, Li X, Wang W, Xiang X, Meng X (2020) A geometric accuracy analysis and tolerance robust design approach for a vertical machining center based on the reliability theory. Measurement 161:107809. https://doi.org/10.1016/j.measurement.2020.107809

    Article  Google Scholar 

  10. Sriramdas V, Chaturvedi SK, Gargama H (2014) Fuzzy arithmetic based reliability allocation approach during early design and development. Expert Syst Appl 41:3444–3449. https://doi.org/10.1016/j.eswa.2013.10.048

    Article  Google Scholar 

  11. Wang W, Xiong J, Xie M (2016) A study of interval analysis for cold-standby system reliability optimization under parameter uncertainty. Comput Ind Eng 97:93–100. https://doi.org/10.1016/j.cie.2016.04.017

    Article  Google Scholar 

  12. Zhang E, Chen Q (2016) Multi-objective reliability redundancy allocation in an interval environment using particle swarm optimization. Reliab Eng Syst Saf 145:83–92. https://doi.org/10.1016/j.ress.2015.09.008

    Article  Google Scholar 

  13. Chatwattanasiri N, Coit DW, Wattanapongsakorn N (2016) System redundancy optimization with uncertain stress-based component reliability: Minimization of regret. Reliab Eng Syst Saf 154:73–83. https://doi.org/10.1016/j.ress.2016.05.011

    Article  Google Scholar 

  14. Gholinezhad H, Hamadani AZ (2017) A new model for the redundancy allocation problem with component mixing and mixed redundancy strategy. Reliab Eng Syst Saf 164:66–73. https://doi.org/10.1016/j.ress.2017.03.009

    Article  Google Scholar 

  15. Chang K-H (2017) A more general reliability allocation method using the hesitant fuzzy linguistic term set and minimal variance OWGA weights. Appl Soft Comput 56:589–596. https://doi.org/10.1016/j.asoc.2016.07.008

    Article  Google Scholar 

  16. Kim KO, Zuo MJ (2018) Optimal allocation of reliability improvement target based on the failure risk and improvement cost. Reliab Eng Syst Saf 180:104–110. https://doi.org/10.1016/j.ress.2018.06.024

    Article  Google Scholar 

  17. Wang H, Zhang Y-M, Yang Z (2018) A reliability allocation method of CNC lathes based on copula failure correlation model. Chin J Mechan Eng 31:111. https://doi.org/10.1186/s10033-018-0303-9

    Article  Google Scholar 

  18. Wang W, Lin M, Fu Y, Luo X, Chen H (2020) Multi-objective optimization of reliability-redundancy allocation problem for multi-type production systems considering redundancy strategies. Reliab Eng Syst Saf 193:106681. https://doi.org/10.1016/j.ress.2019.106681

    Article  Google Scholar 

  19. Modibbo UM, Arshad M, Abdalghani O, Ali I (2021) Optimization and estimation in system reliability allocation problem. Reliab Eng Syst Saf 212:107620. https://doi.org/10.1016/j.ress.2021.107620

    Article  Google Scholar 

  20. Chen Y, Ran Y, Wang Z, Li X, Yang X, Zhang G (2021) Meta-action reliability-based mechanical product optimization design under uncertainty environment. Eng Appl Artif Intell 100:104174. https://doi.org/10.1016/j.engappai.2021.104174

    Article  Google Scholar 

  21. Chen R, Zhang C, Wang S, Qian Y (2021) Reliability estimation of mechanical seals based on bivariate dependence analysis and considering model uncertainty. Chin J Aeronaut 34:554–572. https://doi.org/10.1016/j.cja.2020.12.001

    Article  Google Scholar 

  22. Sun B, Li Y, Wang Z, Yang D, Ren Y, Feng Q (2021) A combined physics of failure and Bayesian network reliability analysis method for complex electronic systems. Process Saf Environ Prot 148:698–710. https://doi.org/10.1016/j.psep.2021.01.023

    Article  Google Scholar 

  23. Fu C, Sayed T (2021) Multivariate Bayesian hierarchical Gaussian copula modeling of the non-stationary traffic conflict extremes for crash estimation. Anal Meth Accid Res 29:100154. https://doi.org/10.1016/j.amar.2020.100154

    Article  Google Scholar 

  24. Yan-Gang Z, Zhao-Hui L (2007) Fourth-moment standardization for structural reliability assessment. J Struct Eng 133:916–924

    Article  Google Scholar 

  25. Lee BH (2001) Using Bayes belief networks in industrial FMEA modeling and analysis. In: Annual Reliability and Maintainability Symposium. 2001 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.01CH37179), pp 7–15

    Chapter  Google Scholar 

  26. Gupta G, Ghasemian H, Janvekar AA (2021) A novel failure mode effect and criticality analysis (FMECA) using fuzzy rule-based method: a case study of industrial centrifugal pump. Eng Fail Anal 123:105305. https://doi.org/10.1016/j.engfailanal.2021.105305

    Article  Google Scholar 

  27. Yang Z, Zhu Y, Ren H, Zhang Y (2015) Comprehensive reliability allocation method for CNC lathes based on cubic transformed functions of failure mode and effects analysis. Chin J Mechan Eng 28:315–324

    Article  Google Scholar 

  28. Huang M, Wang Q, Li Y, Ao L (2011) An approach for improvement of avionics reliability assessment based on copula theory. In: The Proceedings of 2011 9th International Conference on Reliability, Maintainability and Safety, pp 179–183

    Chapter  Google Scholar 

  29. Fang G, Pan R, Hong Y (2020) Copula-based reliability analysis of degrading systems with dependent failures. Reliab Eng Syst Saf 193:106618. https://doi.org/10.1016/j.ress.2019.106618

    Article  Google Scholar 

  30. Kim KO, Yang Y, Zuo MJ (2013) A new reliability allocation weight for reducing the occurrence of severe failure effects. Reliab Eng Syst Saf 117:81–88. https://doi.org/10.1016/j.ress.2013.04.002

    Article  Google Scholar 

  31. Zhao Y-G, Ono T (2001) Moment methods for structural reliability. Struct Saf 23:47–75. https://doi.org/10.1016/S0167-4730(00)00027-8

    Article  Google Scholar 

  32. Liu Y, Li T, Liu K, Zhang Y (2016) Chatter reliability prediction of turning process system with uncertainties. Mech Syst Signal Process 66–67:232–247. https://doi.org/10.1016/j.ymssp.2015.06.030

  33. Zhao Y-G, Ono T (2004) On the problems of the fourth moment method. Struct Saf 26:343–347. https://doi.org/10.1016/j.strusafe.2003.10.001

    Article  Google Scholar 

  34. Zhao Y-G, Lu Z-H (2007) Applicable range of the fourth–moment method for structural reliability. Architectural Institute of Japan, Architectural Institute of Korea, Architectural Society of China

    Book  Google Scholar 

  35. Lu Z-H, Cai C-H, Zhao Y-G, Leng Y, Dong Y (2020) Normalization of correlated random variables in structural reliability analysis using fourth-moment transformation. Struct Saf 82:101888. https://doi.org/10.1016/j.strusafe.2019.101888

    Article  Google Scholar 

  36. Ling C, Lu Z, Cheng K, Sun B (2019) An efficient method for estimating global reliability sensitivity indices. Probabilistic Eng Mech 56:35–49. https://doi.org/10.1016/j.probengmech.2019.04.003

    Article  Google Scholar 

  37. Cadini F, Lombardo SS, Giglio M (2020) Global reliability sensitivity analysis by Sobol-based dynamic adaptive kriging importance sampling. Struct Saf 87:101998. https://doi.org/10.1016/j.strusafe.2020.101998

  38. Zhou Y, Yimin Z, Xianzhen H et al (2013) Reliability-based robust optimization design of automobile components with nonnormal distribution parameters. Chin J Mech Eng 26:823–830. https://doi.org/10.3901/CJME.2013.04.823

  39. Cai L, Zhang Z, Cheng Q et al (2016) An approach to optimize the machining accuracy retainability of multi-axis NC machine tool based on robust design. Precis Eng-J Int Soc Precis Eng Nanotechnol 43:370–386. https://doi.org/10.1016/j.precisioneng.2015.09.001

    Article  Google Scholar 

  40. Kokkinos O, Papadopoulos V (2016) Robust design with Variability Response Functions; an alternative approach. Struct Saf 59:1–8. https://doi.org/10.1016/j.strusafe.2015.10.001

    Article  Google Scholar 

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Funding

This work was supported by the Chinese National Natural Science Foundation (grant nos. U1710119, U1708254) and the Fundamental Research Funds for the Central Universities (N2003022).

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Correspondence to Zhou Yang.

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Yang, Z., Kou, M. Innovation fusion design of mechanical system robust design. Int J Adv Manuf Technol 124, 3795–3811 (2023). https://doi.org/10.1007/s00170-021-07843-4

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  • DOI: https://doi.org/10.1007/s00170-021-07843-4

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