Multi-Operational Machining Processes Modeling for Sequential Root Cause Identification and Measurement Reduction

Abstract

Variation propagation modeling has been proved to be an effective way for variation reduction and design synthesis in multi-operational manufacturing processes. However, previously developed approaches for machining processes did not directly model the process physics regarding how fixture, and datum, and machine tool errors generate the same pattern on part features. Consequently, it is difficult to distinguish error sources at each operation. This paper formulates the variation propagation model using the proposed equivalent fixture error concept. With this concept, datum error and machine tool error are transformed to equivalent fixture locator errors at each operation. As a result, error sources can be grouped and root cause identification can be conducted in a sequential manner. The case studies demonstrate the model validity through a real cutting experiment and model advantage in measurement reduction for root cause identification.