Abstract: Variation analysis for common machining processes aims to reduce variation sources and improve manufacturing quality. Researchers have attempted many methods to recognize the rules of variation sources acting on the machining elements. However, the methods have their own limitations individually due to the complexity of the machining errors. In this paper, a multivariate semi-parametric regression model based on the mathematical analysis and the engineering experiences is proposed to face the variation analysis challenge in common machining processes. The parametric estimation and non-parametric rule identification are discussed in detail based on the measured data. A simulation case indicates that compared with the existing method, the proposed model not only is able to accurately estimate the variation streamed from previous machining stations, but also effectively identify the system errors at current station. The research provides a foundation for variation analysis in common machining processes.

Key words: common machining processes, variation analysis, a multivariate semi-parametric regression model, a least squares kernel smoothing estimation, optimal window width