Abstract
The objective of tolerance analysis is to check the extent and nature of variation of an analyzed dimension or geometric feature of interest for a given GD & T scheme. The parametric approach to tolerance analysis is based on parametric constraint solving. The accuracy of simulation results is dependent on the userdefined modeling scheme. Once an accurate CAD model is developed, it is integrated with tolerance synthesis model. In order to make it cost competent, it is necessary to obtain the costtolerance relationships. The neural network recently has been reported to be an effective statistical tool for determining relationship between input factors and output responses. This study deals development of direct constraint model in CAD, which is integrated to an optimal tolerance design problem. A backpropagation (BP) network is applied to fit the costtolerance relationship. An optimization method based on Differential Evolution (DE) is then used to locate the combination of controllable factors (tolerances) to optimize the output response (manufacturing cost plus quality loss) using the equations stemming from the trained network. A tolerance synthesis problem for a motor assembly is used to investigate the effectiveness and efficiency of the proposed methodology.
Highlights
IntroductionAs one of many design variables, the role of dimensional tolerances is to restrict the amount of size variation in a manufacturing feature while ensuring functionality
Tolerance is the allowable range of variation from design intent in a dimension
An optimization method based on Differential Evolution (DE) is used to locate the combination of controllable factors to optimize the output response using the equations stemming from the trained network
Summary
As one of many design variables, the role of dimensional tolerances is to restrict the amount of size variation in a manufacturing feature while ensuring functionality. Tolerance analysis involves modeling of the relations among variation, tolerance and cost. Tolerance analysis is conducted using variation propagation models that compute how part, subassembly, and process variations propagate to final product variation, which is related to product quality. The analysis include: 1) the contributor, i.e., The dimensions or features that causes variations in the analyzed dimension, 2) the sensitivities with respect to each contributor, 3) the percent contribution to variation from each contributor, and 4) worst case variations, statistical distribution, and acceptance rates. The draftsmen community uses a manual procedure called tolerance charts; but can do only worst-case analysis, and is conducted in only one direction at a time. Many researchers [1,2,3,4] provided good surveys of GD&T modeling for CATs; Some researcher [5] discussed the simplification of feature based models for tolerance analysis, and used the linear programming approach to tolerance analysis involving geo-metric tolerances [5]; Guilford et al [2] introduced a CAT system using the variation modeling and feasibility space approaches
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