Optimization Method for Processes with Adjustment, by Multilayer Regression Networks with Measured Feedback

Abstract

In the manufacturing industry, an adjustment mechanism is sometimes provided in the design of a product with a large degree of individual difference. The term “individual difference” refers to the variation in the final performance of a product caused by the accumulation of intersecting parts and variations in the assembly process. By providing an adjustment mechanism, it is possible to absorb such individual differences by making adjustments during the final performance test. However, such adjustment process depend on the skill of the operator, because the initial conditions vary for each individual. Especially when there are multiple adjustment parameters and final performance parameters, the adjustment process becomes very complicated and the adjustment patterns increase exponentially. In this study, we propose a modeling method that combines a regression algorithm, causal inference, mathematical optimization, and measured value feedback to represent individual differences for the purpose of optimizing the adjustment process. The measured value feedback is a process to compensate for the bias by computing the assignment of measured values to the regression equation. This method can learn efficiently from a small number of machines samples, and succeeds in creating a model that can predict the optimal point in the adjustment of an machine with an unknown individual difference.