SBLMD-ANN-MOPSO-based hybrid approach for determining optimum parameter in CNC milling

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

Automated summary

This paper presents an efficient methodology for chatter detection using Spline-Based Local Mean Decomposition (SBLMD) to decompose the acquired sound signals. The selected decomposed components are then used to reconstruct the new chatter signal, which is rich in information. Additionally, two artificial neural network (ANN) models are established to predict the Cutting Index (CI) and Material Removal Rate (MRR) using three process-related variables. The optimal input parameters are obtained using the MOPSO algorithm.