Research on the multi-sensor fusion-based tool condition recognition system

Multi-sensor fusion improves the accuracy of machine tool condition monitoring system, which is the critical feedback information to the manufacture process controller. An abundant data collected by multi-sensor monitoring system need to employ attribute extraction, election, reduction and classification to form the decision knowledge. A machine tool monitoring system is built and the method of tool condition decision knowledge discovery is presented. Multiple sensors include vibration, force, acoustic emission and main spindle current. The novel approach engages rough theory as a knowledge extraction tool to work on the data that are obtained from both multi-sensor and machining parameters, then extracts a set of minimal diagnostic rules encoding the preference pattern of decision making by domain experts. By means of the knowledge acquired, the tool condition can be identified. A case study is presented to illustrate the effectiveness of the methodology.