Abstract
Tool wear is inevitable in manufacturing and affects the surface quality and geometric tolerance significantly. A robust and efficient tool condition monitoring (TCM) system is needed to maximize tool life, ensure work-piece quality, and benefit the cost control of manufacturers. This paper presents a systematic singularity analysis approach of cutting force and vibrations for feature extraction of TCM in milling. The singularity of sensory signals is estimated by Holder Exponents (HE), which are determined by wavelet transform modulus maxima (WTMM). A comprehensive wavelet basis selection approach is proposed to choose the appropriate wavelet basis for different sensory signals. A de-noising algorithm based on WTMMs' estimation was used as a pre-processing technique to improve noise reduction and preserve the singularities. The mutual information method was employed to rank HE features. The effectiveness of the singularity analysis approach is validated through the Support Vector Machine (SVM) models trained by these ranked features. The estimating results of case studies confirm the efficacy and efficiency of the proposed approach.
Highlights
Manufacturing sectors play an essential role since they are among the largest energy consumers in modern societies [1]
The wavelet basis with 1 vanishing moment is found quite efficient to analyze cutting force signals, and the wavelet basis with 2 vanishing moments is most suitable for vibration signals
A comprehensive wavelet basis selection approach is proposed to decide which order vanishing moment is appropriate for different sensory signals without any knowledge of their singularity properties
Summary
Manufacturing sectors play an essential role since they are among the largest energy consumers in modern societies [1]. The high-end manufacturing industry, such as aviation and aerospace fields, has always been the vane of manufacturing development due to its technological advantages. In these fields, materials with excellent properties such as titanium alloys and nickel-based alloys are widely applied. Tolerance are highly related to the tool conditions, especially when high-added-value components like aeronautical engine blades and monolithic parts, are manufactured. To ensure the machining quality of high value-added components, workers often tend to adopt conservative tool replacement strategies. A robust and efficient tool condition monitoring (TCM) system is needed to maximize tool life, ensure work-piece quality, and benefit the cost control of manufacturers [2]
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