Automatic Process Adjustment for Reducing Dimensional Variation in Discrete Part Machining Processes

Abstract

Traditional Statistical Process Control (SPC) has been widely employed for process monitoring in discrete part manufacturing. However, control charts do not consider any adjustment preventing the process drift from target. 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 a control algorithm that can automatically reduce the overall process variations. Considering three types of error sources in a machining process, we propose to use fixture locators to introduce process adjustment based on our previously proposed Equivalent Fixture Error (EFE) concept. The dynamic property of EFE is investigated for feedback adjustment of both kinematic and quasi-static errors in machining processes. A Minimum-Mean-Square-Error (MMSE) controller is designed based on the dynamic EFE model. We then evaluate the performance of the controller such as stability and sensitivity. Self-updating algorithm for controller has been proposed to track the latest process information as well. Finally, we simulate this process adjustment using the data collected from a real machining process. The results show that this algorithm can effectively improve the machining accuracy and reduce the process variations.