Using Error Equivalence Concept to Automatically Adjust Discrete Manufacturing Processes for Dimensional Variation Control

Abstract

Traditional statistical process control (SPC) has been widely employed for the process monitoring in discrete part manufacturing. However, SPC does not consider any adjustment preventing the process drift. Furthermore, many in-line adjustment approaches, such as thermal error compensation and avoidance, are designed only for machine tool error reduction. This paper intends to fully utilize the engineering process information and to propose an alternative compensation strategy based on equivalent fixture error (EFE) concept that could reduce overall effect of the process errors. Considering three types of error sources in a machining process, we propose to adjust fixture locators to compensate errors using the EFE model. The dynamic property of EFE is investigated for the feedback adjustment of both static and quasi-static errors in machining processes. A minimum-mean-square-error controller is designed based on the dynamic EFE model. We then evaluate the performance of the controller such as stability and sensitivity. A self-updating algorithm for the controller will track the latest process information to stabilize the controller output. Finally, we simulate this process adjustment using the data collected from a real machining process. The results show that this algorithm can improve the machining quality.