The primary objective of this research is to investigate a number of enabling technologies for a smart machine tool: 1. on-line calibration of cutting force models using non-intrusive sensors, 2. tool condition monitoring based on cutting energy analysis, 3. chatter prediction and prevention, 4. information technology for representation of machining process plans and 5. novel inexpensive non-intrusive sensors. The research approach is based on combining sensor data and mathematical models along with unique on-line calibration methods, and a web-based method for data representation of machine tools and process plans. Experimental characterization of machine dynamics for the purpose of chatter analysis will be combined with time domain simulation to choose chatter-free machining conditions. The use of an Open Architecture Controller on a numerically controlled milling machine testbed facility allows mathematical models of the process to be continuously updated by sensor data input. Extensive experimental studies will be performed in collaboration with industrial partners to test the applicability of the method to a wide variety of cutting conditions, cutting tools and workpiece materials.
If successful, this research will have the following broad impacts: 1. greatly improve the reliability of machine tools by self-knowledge of their process capabilities, 2. Self-knowledge of process capabilities will allow matching of specific machines with the requirements of a particular machining operation, 3. Machining strategies and cutting conditions (speeds, feeds and cutting depths) can be chosen to appropriately reflect the current capabilities of the machine tool. 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 strengthen the nation's manufacturing community.
