A surrogate model-assisted robustness-oriented tolerance design method based on 'reverse model'

The uncertainties of parameters are inevitable in practical engineering problems, which may cause unexpected variations of objectives and constraints. Tolerance design methods can specify the allowable variation of uncertain parameters, thereby maintaining product quality while achieving lower manufacturing cost. In this paper, a novel tolerance design method is proposed, in which the 'reverse model' is used to directly analyse the robustness of the preexisting alternatives and the Kriging model is introduced to reduce computational cost. Firstly, the sensitivity region is generated by the 'reverse model' that maps the given acceptable objective and feasibility variations into the parameter space to analyse the robustness. Secondly, interval uncertainty is introduced to describe tolerance within the sensitive region. Thirdly, a nested double-loop structure is adopted to implement the search process. Two numerical examples and one engineering case are used to illustrate the effectiveness of the proposed method. Verification of robustness for the obtained interval is performed by Monte Carlo Method, and the worst-case analysis is applied to verify whether the tolerance is the maximum interval. The obtained maximum tolerance that meets the performance requirements reflects the trade-off between quality and manufacturing cost.

Automated summary

The paper proposes a novel tolerance design method using a 'reverse model' and Kriging model to analyze robustness and reduce computational cost. It utilizes sensitivity region and interval uncertainty to describe tolerance range and employs a nested double-loop structure for the search process. The effectiveness of the method is demonstrated through numerical examples and an engineering case study.