Nonlinear Assembly Tolerance Design for Spatial Mechanisms Based on Reliability Methods
Nonlinear Assembly Tolerance Design for Spatial Mechanisms Based on Reliability Methods
复制标题
基于可靠性方法的空间机构非线性装配公差设计
DOI：
10.1115/1.4035433
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发表时间：
2017-03
影响因子：
3.3
通讯作者：
Ni Huajin
中科院分区：
文献类型：
--
作者：
Yin Yin;Hong Nie;Fei Feng;Wei Xiaohui;Ni Huajin
空间机构装配公差设计是一个复杂的工程问题，涉及高度非线性的尺寸链方程和简化空间机构矩阵方程的难题。针对该问题的非线性和尺寸链方程难以简化的问题，分别研究了Rackwitz-Fiessler(R-F)可靠性分析方法和几种替代模型方法的应用。用R-F法和代理模型法对起落架装配问题进行的公差分析结果表明，与极值法和概率法相比，R-F法能更准确、更高效地计算装配成功率，合理的公差分配设计，成本分别降低37%和16%。基于代理模型的计算结果表明，在三种代理方法中，支持向量机方法的计算精度最高，但计算时间较长，而响应面方法和反向传播神经网络方法的计算精度较低，但计算效率较高。总体而言，与尺寸链方程的简化或蒙特卡罗方法相比，所有的替代模型方法都提供了良好的计算精度，同时所需的分析和计算时间要少得多。
Assembly tolerance design for spatial mechanisms is a complex engineering problem that involves a highly nonlinear dimension chain equation and challenges in simplifying the spatial mechanism matrix equation. To address the nonlinearity of the problem and the difficulty of simplifying the dimension chain equation, this paper investigates the use of the Rackwitz–Fiessler (R–F) reliability analysis method and several surrogate model methods, respectively. The tolerance analysis results obtained for a landing gear assembly problem using the R–F method and the surrogate model methods indicate that compared with the extremum method and the probability method, the R–F method allows more accurate and efficient computation of the successful assembly rate, a reasonable tolerance allocation design, and cost reductions of 37% and 16%, respectively. Moreover, the surrogate-model-based computation results show that the support vector machine (SVM) method offers the highest computational accuracy among the three investigated surrogate methods but is more time consuming, whereas the response surface method and the back propagation (BP) neural network method offer relatively low accuracy but higher calculation efficiency. Overall, all of the surrogate model methods provide good computational accuracy while requiring far less time for analysis and computation compared with the simplification of the dimension chain equation or the Monte Carlo method.
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影响因子：
2.5
作者：
Chwail Kim;Semyun Wang;Il-Han Hwang;Jong-Hyun Lee
通讯作者：
Chwail Kim;Semyun Wang;Il-Han Hwang;Jong-Hyun Lee
影响因子：
5.2
作者：
U. Kumaraswamy;M. Shunmugam;S. Sujatha
通讯作者：
U. Kumaraswamy;M. Shunmugam;S. Sujatha
影响因子：
2.5
作者：
Chwail Kim;Semyun Wang;K. Choi
通讯作者：
Chwail Kim;Semyun Wang;K. Choi
影响因子：
4.1
作者：
Hurtado, JE;Alvarez, DA
通讯作者：
Alvarez, DA
影响因子：
4.7
作者：
RACKWITZ, R;FIESSLER, B
通讯作者：
FIESSLER, B