A sensor fusion and support vector machine based approach for recognition of complex machining conditions

Changqing Liu, Yingguang Li (), Guanyan Zhou and Weiming Shen ()
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Changqing Liu: Nanjing University of Aeronautics and Astronautics
Yingguang Li: Nanjing University of Aeronautics and Astronautics
Guanyan Zhou: Nanjing University of Aeronautics and Astronautics
Weiming Shen: University of Western Ontario

Journal of Intelligent Manufacturing, 2018, vol. 29, issue 8, No 4, 1739-1752

Abstract: Abstract During the machining process of thin-walled parts, machine tool wear and work-piece deformation always co-exist, which make the recognition of machining conditions very difficult. Existing machining condition monitoring approaches usually consider only one single condition, i.e., either tool wear or work-piece deformation. In order to close this gap, a machining condition recognition approach based on multi-sensor fusion and support vector machine (SVM) is proposed. A dynamometer sensor and an acceleration sensor are used to collect cutting force signals and vibration signals respectively. Wavelet decomposition is utilized as a signal processing method for the extraction of signal characteristics including means and variances of a certain degree of the decomposed signals. SVM is used as a condition recognition method by using the means and variances of signals as well as cutting parameters as the input vector. Information fusion theory at the feature level is adopted to assist the machining condition recognition. Experiments are designed to demonstrate and validate the feasibility of the proposed approach. A condition recognition accuracy of about 90 % has been achieved during the experiments.

Keywords: Intelligent machining; Machining condition recognition; Sensor fusion; Support vector machine; Wavelet decomposition (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10845-016-1209-y

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