Cost-tolerance analysis model based on a neural networks method

The purpose of tolerance design in product components is to produce a product with the least manufacturing cost possible, while meeting all functional requirements of the product. The product designer and process planner must fully understand the process accuracy and manufacturing cost of all kinds of manufacturing process to perform a good process plan job. Usually, the cost–tolerance model is constructed by a linear or non-linear regression analysis based on the data of the cost–tolerance experiment and to derive the correlation curve between the two. Though these correlation curves can show the relationship between manufacturing cost and tolerance, a fitting error is inevitable. In particular, there is considerable discrepancy in terms of the non-experimental data. A cost–tolerance analysis model based on a neural networks method is proposed. The cost–tolerance experimental data are used to set the training sets to establish a cost–tolerance network. Three representation modes of the cost–tolerance relationship are presented. First, the cost–tolerance relationship is derived from the grid points setting by the required tolerance accuracy. Second, a reasonable manufacturing cost of an unknown cost–tolerance experimental pair can be derived by the simulation of a cost–tolerance network. Third, an inference model based on a network's output is proposed to express the scope of the cost variation of various tolerances by means of a cost band. Comparison is also made with the high-order poly-nomial power function and exponential function cost–tolerance curves adopted by Yeo et al. Analytical results prove that the application of the cost–tolerance analysis model based on neural networks yields better performance in controlling the average fitting error than all conventional fitting models. The representation model using a cost band can identify precisely the possible cost variation range and reduce the chances of error in the tolerance design and cost estimation. It can thus provide important references for tolerance designers and process planners.