To reduce operating costs and improve product quality are two objectives for the modern manufacturing industries, so most manufacturing systems are fast converting to fully automated environments such as computer integrated manufacturing (CIM) and flexible manufacturing systems (FMS). However, many manufacturing processes involve some aspects of metal cutting operations. The most crucial and determining factor to successful | Li Xiaoli Fuzzy Neural Network and Wavelet for Tool Condition Monitoring Computational Intelligence in Manufacturing Handbook Edited by Jun Wang et al Boca Raton CRC Press LLC 2001 15 Fuzzy Neural Network and Wavelet for Tool Condition Monitoring Xiaoli Li Harbin Institute of Technology Introduction Fuzzy Neural Network Wavelet Transforms Tool Breakage Monitoring with Wavelet Transforms Identification of Tool Wear States Using Fuzzy Methods Tool Wear Monitoring with Wavelet Transforms and Fuzzy Neural Network Introduction To reduce operating costs and improve product quality are two objectives for the modern manufacturing industries so most manufacturing systems are fast converting to fully automated environments such as computer integrated manufacturing CIM and flexible manufacturing systems FMS . However many manufacturing processes involve some aspects of metal cutting operations. The most crucial and determining factor to successful maximization of the manufacturing processes in any typical metal cutting process is tool condition. It would seem be logical to propose that tool condition monitoring TCM will inevitably become an automated feature of such manufacturing environments. Due to failure cutting tools adversely affect the surface finish of the workpiece and damage machine tools serious failure of cutting tools may possibly endanger the operator s safety. Therefore it is very necessary to develop tool condition monitoring systems that would alert the operator to the states of cutting tools thereby avoiding undesirable consequences 1 . Initial TCM systems focused mainly on the development of mathematical models of the cutting process which were dependent upon large amounts of experimental data. Due to the complexity of the metal cutting process an accurate model for wear and breakage prediction of cutting tools cannot be obtained so that many researchers resort to sensor integration methods for replacing model methods. .
