Tool condition monitoring (TCM) is a necessary action in a high-speed milling (HSM) process. As a worn milling tool might irreversibly damage a workpiece, there is a vital demand for a TCM system to evaluate the tool wear progress, or equivalently the resultant surface roughness, nonintrusively. To build up a condition monitoring system for HSM processes, sensor signals are to be utilized to form a reference model that reflects the performance of the system. Therefore, a desired reference model has to apply more efficient feature extraction and artificial intelligence techniques to be more repeatable and generalizable. This paper illustrates the performance of clustering techniques on high-speed end milling experimental data. Studied clustering methods are applied to the wavelet features of force and vibration signals to illustrate the repeatability of their results. It is shown that clustering methods can coarsely capture the status of the process and can be applied for fault diagnosis and TCM purposes. It is also discussed how the application of clustering methods may improve the performance of existing reference models toward the more efficient utilization of available experimental data and to develop easily generalizable reference models. Finally, a possible application of clustering results is discussed comparing with state-of-the-art papers.
High Speed Machining centers (HSM) are considered as complicated industrial instruments. Finishing is a critical process in production procedure which is carried on by these machines. Among many types of cutters, ball-nose cutters are the preferred cutters to do these kinds of operations since they have extensive operating cutting edges and appropriate geometry. The main aim of the researches on cutting process is to understand its nature better and to use this knowledge to improve the quality of the product. To achieve this goal, it is necessary to have a descriptive reference model on the process using experiments' data. Increasing demands for better surfacefinishing and concurrently the development of the available measurement instruments and modeling techniques make the methods and approaches to be novel. Present paper is a survey on the lack of literature on the state-of-the-art modeling paradigms of milling processes, mainly on ball-nose cutters for surface finishing.
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