Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Manuscript received April 4, 2023; revised May 6, 2023; accepted June 25, 2023.
Abstract—The cutting force of the aluminum workpiece was forecasted using the Artificial Neural Networks (ANNs) methodology in this study. Two ANN structures, one with a single hidden layer and the other with a double hidden layer, were constructed using MATLAB codes. The Levenberg- Marquardt back-propagation technique served as the training algorithm, employing a sigmoidal transfer function in the hidden layer and a purline transfer function in the output layer. The performance of the ANN models was assessed using Mean Squared Error (MSE) and coefficient of determination (R2). The experimental findings revealed that the cutting speed, feed rate, and depth of cut significantly influenced the cutting force. The optimal number of neurons in both single and double hidden layers was determined to be 6. The validation stage achieved the best performance with an MSE of approximately 0.002747 for a single layer and 0.00144 for double hidden layers, both at epoch 5. In conclusion, both ANN structures demonstrated the capability to predict cutting force, with a preference for the double hidden layer structure.
Keywords—depth of cut, feed, cutting force, artificial neural network
Cite: Dawood S. Mahjoob, Ahmad A. Khalaf, and Muammel M. Hanon, "Forecasting Cutting Force by Using Artificial Neural Networks Based on Experiments of Turning Aluminum," International Journal of Mechanical Engineering and Robotics Research, Vol. 12, No. 6, pp. 410-416, November 2023.
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