Journal of Intelligent Manufacturing

, Volume 29, Issue 8, pp 1739–1752 | Cite as

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

  • Changqing Liu
  • Yingguang Li
  • Guanyan Zhou
  • Weiming Shen


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.


Intelligent machining Machining condition recognition Sensor fusion Support vector machine Wavelet decomposition 



The research work presented in this paper was primarily supported by the National Natural Science Foundation of China (Ref: 51375239, U1537209), and New Century Excellent Talents Supporting Plan of the Education Ministry (Ref: NCEP-13-0856).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Changqing Liu
    • 1
    • 2
  • Yingguang Li
    • 1
  • Guanyan Zhou
    • 1
  • Weiming Shen
    • 2
    • 3
  1. 1.College of Mechanical and Electrical EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Department of Electrical and Computer EngineeringUniversity of Western OntarioLondonCanada
  3. 3.The Key Laboratory of Embedded System and Service ComputingTongji UniversityShanghaiChina

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