SBLMD–ANN–MOPSO-based hybrid approach for determining optimum parameter in CNC milling
نویسندگان
چکیده
Chatter is a type of self-excited vibration that expresses variations in frequency and energy dispersion during the milling process invariably results poor part quality lower material removal rate. An efficacious chatter detection approach necessary to anticipate chatter's nascent stage. Feature extraction an important step identifying chatter. In this paper, efficient PF-based multi-mode signal processing method, i.e., Spline-Based Local Mean Decomposition (SBLMD), has been used decompose experimentally acquired sound signals into series PFs then, selected have reconstruct new signal, which rich information. Further, two three-layer ANN-based prediction models are established predict CI MRR. Three process-related variables, such as tool speed, feed cut depth, develop ANN models. Statistical comparisons conducted obtain optimal training algorithm found LM-based was best among five other algorithms. For estimates MRR, neurons hidden layer optimized at 5 7 with least mean squared errors (MSE), respectively. After applying MOPSO models, MOPSO-optimized ANNs very sensitive optimizing input layers. The ranges for Cut Depth (CD), Feed Speed (FS) Tool (TS) 1.56–1.77 mm, 77–97 mm/min, 2380–2880 rpm,
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ژورنال
عنوان ژورنال: Soft Computing
سال: 2023
ISSN: ['1433-7479', '1432-7643']
DOI: https://doi.org/10.1007/s00500-023-07944-0