The primary objective of this research is to investigate novel techniques and strategies for dynamically measuring the process capability of machine tools. A secondary purpose is to investigate the use of process capability information to accurately estimate machining costs. The research approach combines real-time sensor data with unique cutting force process models that are both accurate and computationally efficient. Process capability will be estimated by comparing measured cutting forces with concurrent model simulation results. An open-architecture machine tool controller will be used to collect and store measurement data, run model simulations, and dynamically update the process capability. Extensive experimental studies will focus on three parameters deemed to be the most difficult to characterize: tool runout, tool deflection and tool wear. Generality will be tested with an experimental matrix that includes a variety of different cutting conditions, cutter types, and ferrous and non-ferrous alloy materials.
Successful completion of this research project would improve the reliability, accuracy and efficiency of machine tools by giving them self-knowledge of their process capabilities. Process capability information could also be used to accurately estimate machining costs, thereby providing valuable feedback to designers about the cost implications of their design choices. Process capabilities of a specific machine tool could be intelligently matched with part tolerance requirements to ensure defect free production. Part quality could be maintained over time by adjusting cutting strategies and conditions in response to changing process capability. This project will also support the Smart Machine Initiative of the National Institute of Standards and Technology and is consistent with the goals of the Integrated Manufacturing Initiative, a public/private consortium of industry, academic, and government partners designed to strength the nation's manufacturing community.
