Dimensional variation propagation and accumulation in multistage manufacturing processes are among the most important issues that affect quality. Although robust design and statistical process quality control help to reduce the effects of these problems, neither of these two methods can be used for instant variation reduction during assembly operations. This paper introduces a complete methodology for error compensation in compliant sheet metal assembly processes. The proposed methodology can be divided in two steps: (1) an off-line error control-learning module using virtual assembly models, and (2), an in-line control implementation using a feedforward control strategy. The off-line learning method focuses on determining the optimal control actions or corrections to a set of predefined deviations. Specifically, it utilizes a newly developed iterative sampling method based on Kriging fitting to efficiently determine an optimal control action. The in-line feedforward control uses measurements of incoming assembly components to select an appropriate pre-learned control action. Two case studies are presented; first, a mathematical case study is used as the empirical proof for the feasibility of the iterative sampling and fitting algorithm. Second, a simulation-based case study is used to illustrate the effectiveness of the proposed methodology to improve dimensional quality in assembly operations of compliant sheet metal parts.

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