This paper investigates and analyzes the wear on an end mill cutting tool. Current tool condition monitoring systems lack efficient and effective fault prediction and diagnosis. To achieve adoption in the machining industry, condition monitoring must be accurate, non-invasive and cost effective with a longer operational lifetime of tool wear desired. The concept in this work is to associate an acoustic signature to a tool wear condition. First, the application of a Short Time Fourier Transform (STFT) as a time-frequency method to the acoustic measurement that can provide more information throughout the milling operation and gives a better representation of the signal than the conventional methods. Then a support vector machine (SVM) technique is applied as a classification approach to improve condition monitoring of the tool wear. This preliminary research’s goal is to create an accurate prognostic tool to determine tool wear condition through a noninvasive means with the ability to retrofit existing mills with the developed technology. The methodology is validated by actual end mill operation of the cutting of an aluminum workpiece at various feed rates, spindle speeds, and cutting depth.
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