Artificial Neural Network Modelling for AISI 4340 Surface Roughness Analysis

This chapter delves into the intricate world of surface roughness analysis for AISI 4340 steel using Artificial Neural Networks (ANN) and the hot-turning method. It begins with an introduction to AISI 4340, a high-strength low-alloy steel widely used in critical components like gear systems and crankshafts. The research evaluates the performance of this material under various machining conditions, with a particular focus on dry machining, which offers significant environmental and cost benefits. The chapter explores the development of mathematical models using Response Surface Methodology (RSM) and ANN to determine the relationship between independent and dependent variables in machining processes. The core of the chapter is dedicated to the application of ANN, inspired by the biological nervous system, to predict and optimize machining outcomes. The ANN model, based on the Back Propagation (BP) algorithm and the Levenberg-Marquardt algorithm, demonstrates exceptional predictive accuracy, outperforming traditional methods. The results show that the ANN model with a 4-17-1 structure yields the best prediction results for surface roughness, with a Mean Square Error (MSE) of 0.004393106 and a prediction error of 3.795222%. The chapter also provides a detailed comparison of different network structures and activation functions, highlighting the importance of proper selection for optimal performance. The findings contribute to the advancement of sustainable manufacturing and offer valuable insights into the optimization of machining processes, making this chapter a must-read for those interested in the intersection of artificial intelligence and materials science.