Nonlinearity Analysis of Multi-Station Assembly Processes

Linearization is commonly employed in modeling of multi-station manufacturing processes for quality improvement purposes. However, an analysis of the accuracy of the linear models is inevitable before they could be used, because practical manufacturing processes are inherently nonlinear. Such analysis is not straightforward for the high dimensionality of multi-station manufacturing processes and the complicated relationship between process parameters and the process output. This paper presents a nonlinearity analysis method for multi-station assembly processes through an integration of experiment design and data mining. It is found that certain design parameters (e.g., the ratio between locator deviations and the distance between locators) and the number of stations play an important role in the linearization error. Although demonstrated in the specific context of two dimensional multi-station assembly processes, the proposed method can be applied to investigate many characteristics of a broad variety of manufacturing process models.