Modelling of cutting force and surface roughness of ultrasonic-assisted drilling using artificial neural network

Abstract

Abstract This paper presents artificial neural network modelling for the thrust force in terms of maximum and mean values and the surface roughness for drilling soda glass using ultrasonic-assisted drilling. The experimental parameters are the tool concentrations (normal and high), cutting speed, and feed rate. The feedforward architecture neural network is composed of 10 hidden layers with sigmoid function and output layer with linear function. Three models are developed for each response individually and then one model for the three outputs. The models between the neural network output and the target (experimental results) for training, validation, and test data are developed, and their coefficients of regression are reasonable for this experimental data. The suitable number of hidden layers is examined with mean square error, and it is found that it decreases with increasing the number of hidden layer. The three models are developed based on one output, and the model of the three outputs is very close and good representative for the experimental results. It is concluded that the variables can be controlled and optimized by the same conditions.

Keywords

• Neural network
• Ultrasonic-assisted drilling
• Feedforward
• Soda glass
• Thrust force