Abstract
Fixture plays an important role in enhancing the weld quality of the sheet metal assembly process. However, the traditional experience-based fixturing scheme and the purely optimal fixturing scheme are often sensitive to location fluctuation of the designed locators. In this paper, the three quality design models of a non-linear programming model, a polynomial response surface methodology (RSM) and a neural network (NN)-enhanced RSM are presented for fixture planning of a sheet metal assembly with resistance spot weld. In the non-linear programming model, both performance and variance are considered in the formulation of objective function. The polynomial RSM is used to fit a feasible response surface by 3 k fractional factorial design and analysis of the variance; by inspecting the influence of each design variable, one can gain insight into the existence of multiple design choices and select the optimum design based on more factors. NN can be used to confidently generate additional design points added to the original data set to form the enhanced data set; the NN-enhanced RSM can help to improve the accuracy. An industrial case study of a car door assembly is used to illustrate the feasibility of the presented quality design models. This work provides a basis for improving the quality of the body-in-white assembly process in the design phase.
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