Tolerance allocation under behavioural simulation uncertainty of a multiphysical system

Abstract The tolerancing process impacts the product quality, the production cost and scrap rate. Tight tolerances allow to assure product performance; loose tolerances to reduce production cost. The tolerance allocation of a complex system is performed under uncertainty. In fact, the accuracy of the behaviour simulation of the system significantly affects the tolerance analysis result, and thus the tolerance allocation result. Therefore, a method is proposed to perform tolerance allocation based on the Dempster Shafer theory, Monte-Carlo simulation and genetic algorithm. The application of the proposed framework is demonstrated through a complex case study.

[1]  Rikard Söderberg,et al.  Toward a Digital Twin for real-time geometry assurance in individualized production , 2017 .

[2]  Stefano Petrò,et al.  Early cost estimation for tolerance verification , 2011 .

[3]  P K Jain,et al.  Comparative study of genetic algorithm and simulated annealing for optimal tolerance design formulated with discrete and continuous variables , 2005 .

[4]  Enrico Savio,et al.  Economic benefits of metrology in manufacturing , 2016 .

[5]  A. Dumas,et al.  Impact of a behavior model linearization strategy on the tolerance analysis of over-constrained mechanisms , 2015, Comput. Aided Des..

[6]  Lazhar Homri,et al.  Multiphysical tolerance analysis – Assessment technique of the impact of the model parameter imprecision , 2020 .

[7]  Sandro Wartzack,et al.  On uncertainties in simulations in engineering design: A statistical tolerance analysis application , 2014, International Conference on Advances in System Simulation.

[8]  Sandro Wartzack,et al.  Sampling-based Tolerance-Cost Optimization of Systems with Interrelated Key Characteristics , 2020 .

[9]  Jean-Yves Dantan,et al.  Cost engineering for variation management during the product and process development , 2017 .

[10]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[11]  Giovanni Moroni,et al.  Tolerancing: Managing uncertainty from conceptual design to final product , 2018 .

[12]  Sandro Wartzack,et al.  Shaping the digital twin for design and production engineering , 2017 .

[13]  P. Jain,et al.  Important issues in tolerance design of mechanical assemblies. Part 2: Tolerance synthesis , 2009 .

[14]  Sandro Wartzack,et al.  On Connected Tolerances in Statistical Tolerance-Cost-Optimization of Assemblies with Interrelated Dimension Chains , 2016 .

[15]  Nathan W. Hartman,et al.  Allocation of assembly tolerances to minimize costs , 2019, CIRP Annals.

[16]  Jean-Yves Dantan,et al.  Improved algorithm for tolerance allocation based on Monte Carlo simulation and discrete optimization , 2009, Comput. Ind. Eng..