当前位置: X-MOL 学术J. Comput. Des. Eng. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
An iterative method of statistical tolerancing based on the unified Jacobian–Torsor model and Monte Carlo simulation
Journal of Computational Design and Engineering ( IF 4.9 ) Pub Date : 2020-04-02 , DOI: 10.1093/jcde/qwaa015
Heping Peng 1 , Zhuoqun Peng 2
Affiliation  

This paper focuses on exploring an iterative method of statistical tolerance design to guide designers to select tolerances more economically and effectively. After having identified the assembly functional requirement (FR) and the functional elements (FEs) of corresponding tolerance chain, the expression of a unified Jacobian–Torsor model can be derived. Monte Carlo simulation is employed to generate random variables simulating the variations of small displacement torsor associated with the FE pairs with all the generated random values being within the intervals constrained by the corresponding tolerance zones. Then, the real multiplication operations are repeatedly executed to this model, a large number of real torsor component values of FR will be obtained and we can perform statistical analysis for these simulated data to get the statistical limits of the assembly FR in the desired direction. The tolerances of critical FEs may need to be adjusted to satisfy the assembly FR imposed by the designer, and the percentage contribution of each FE to the assembly FR can help determine these critical tolerances that need to be tightened or loosened. Once the calculated FR is in close agreement with the imposed FR, the iterative process can be stopped, and the statistical tolerance redesign is achieved. The effectiveness of the proposed method is illustrated with a case study. Compared with the deterministic tolerancing method, the results show that the proposed method is more economical and that can relax significantly the precision required, manufacturing and inspection costs can then be reduced considerably.

中文翻译:

基于统一Jacobian-Torsor模型和蒙特卡洛模拟的统计容忍迭代方法

本文着重探讨一种统计公差设计的迭代方法,以指导设计人员更经济有效地选择公差。在确定了装配功能要求(FR)和相应公差链的功能要素(FE)之后,可以得出统一的Jacobian-Torsor模型的表达式。蒙特卡罗模拟用于生成随机变量,该随机变量模拟与FE对相关的小位移扭矩的变化,所有生成的随机值都在相应公差范围限制的间隔内。然后,对该模型重复执行实数乘法运算,将获得大量的FR的实际扭转分量值,我们可以对这些模拟数据进行统计分析,以获得所需方向上的组件FR的统计极限。可能需要调整关键FE的公差,以满足设计者施加的组件FR,每个FE对组件FR的百分比贡献可以帮助确定需要拧紧或放松的这些关键公差。一旦计算出的FR与施加的FR紧密一致,就可以停止迭代过程,并实现统计公差的重新设计。案例研究说明了该方法的有效性。与确定性容差方法相比,
更新日期:2020-04-02
down
wechat
bug