ABSTRACT
Summary
Sensors are indispensable in the condition monitoring of mechatronic systems. A milling machine is a mechatronic system. This chapter presents an intelligent integrated approach combining a direct sensor (vision) and an indirect sensor (force) for online monitoring of flank wear and breakage of the cutting tool in milling. For flank wear, images of the tool are captured and processed in-cycle using successive moving image analysis. Two features of the cutting force are extracted in-process and appropriately preprocessed. A self-organizing map (SOM) network is trained in a batch mode after each pass, using the two features derived, and measured wear values obtained by interpolating the vision-based measurement. The trained SOM network is applied to the succeeding machining pass to estimate the flank wear in-process. The in-cycle and in-process procedures are employed alternatively for the online monitoring of the flank wear. To detect breakage, two other features in the time domain, as derived from cutting force, are used. Vision is used to verify whether this breakage has actually occurred. Experimental results show that this sensor fusion scheme is independent of cutting conditions, and is feasible and effective for the implementation of online tool condition monitoring (TCM) in milling.
