Volume 18, Issue 5 (2018)                   Modares Mechanical Engineering 2018, 18(5): 192-201 | Back to browse issues page

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Sadikhani O, Hashemian S A. Nonlinear computer-aided tolerance analysis of mechanical assemblies using improved second-order method. Modares Mechanical Engineering. 2018; 18 (5) :192-201
URL: http://journals.modares.ac.ir/article-15-15184-en.html
1- Department of Mechanical Engineering, Hakim Sabzevari University, Sabzevar, Iran
2- Department of Mechanical EngineeringFaculty of EngineeringHakim Sabzevari University
Abstract:   (272 Views)
Tolerance analysis plays a crucial role in predicting the quality of products and reducing production costs. This procedure is generally complex and available methods for analyzing different types of assemblies are not always applicable. Accordingly, having a comprehensive approach to assess the effect of tolerances on the quality and cost of products is a fundamental requirement in the manufacturing industry. This paper proposes the improved second-order method for tolerance analysis of complex assemblies. The conventional second-order tolerance analysis (SOTA) is an accurate and applicable method for obtaining the statistical specifications of the assembly’s key characteristic. However, determining the assembly function in SOTA entails forming vector loops and therefore, this method is limited to simple assemblies. On the other hand, in mechanical assemblies that are usually complex, creating vector loop may encounter some difficulties in practice. In this study, the mentioned issues have been overcome by linking SolidWorks and MATLAB software to employ the proposed methodology for any mechanical assemblies without creating vector loops. For this purpose, MATLAB software makes necessary changes in the SolidWorks model and calculates the derivatives of the assembly function, which are required for the analysis. Then, the statistical moments are computed and the probability distribution of the key characteristic is obtained using the Pearson system. The present study is appropriate for analyzing either linear or nonlinear assembly functions with any statistical distribution. Finally, the applicability of the proposed approach is investigated by some practical examples and the accuracy of results is confirmed by Monte Carlo simulation.
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Article Type: Research Article |
Received: 2018/01/25 | Accepted: 2018/03/9 | Published: 2018/09/24

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