Abstract
In the present study, acquired audio signal in milling operation is processed in order to extract tool chatter features. Further, six artificial neural network (ANN) training algorithms viz. Resilient propagation (RP), conjugate gradient based (CGP and SCG), quasi-Newton based (BFGS and OSS) and Levenberg-Marquardt algorithm (LM) are used to train the data set. Among these, the most suitable one has been selected and further invoked to develop prediction model of chatter severity in terms of chatter indicator (CI). Finally, developed prediction model has been critically explored to analyze milling stability conditions.
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Rohit Mishra is a Post Graduate of Mechanical Engineering from Indian Institute of Technology, Roorkee, India. His research interests include stability analysis, tool condition monitoring and signal processing.
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Mishra, R., Singh, B. Prediction of milling chatter using SBLMD-ANN. J Mech Sci Technol 36, 877–882 (2022). https://doi.org/10.1007/s12206-022-0135-5
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DOI: https://doi.org/10.1007/s12206-022-0135-5