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Statistical tolerance analysis using FRPDF and numerical convolution
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DOI：10.1016/0010-4485(96)00005-X.png)
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Abstract
En 中文
Computer-aided statistical tolerancing methods have helped to improve the quality of products while reducing their manufacturing costs. The Monte-Carlo simulation is the most popular statistical tolerancing technique currently in use. However, simulation has a severe drawback in terms of speed. Other tolerancing methods such as moment methods have limited capabilities in tackling many manufacturing problems. The objective of this paper is to demonstrate the feasibility of a new methodology for statistical tolerance analysis. The proposed method makes use of a new probability distribution function to model non-normal manufacturing data and a numerical method to perform statistical tolerance stack-up analysis. Comparative studies show that this new method has better accuracy than existing moment based techniques and is faster than simulation. It appears that it can be used in future computer-aided tolerancing systems. Copyright (C) 1996 Elsevier Science Ltd
Keywords:
tolerance analysis
Monte-Carlo simulation
nonnormal distribution
numerical convolution
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