Investigation in optimization of process parameters in turning of mild steel using response surface methodology and modified deep neural network

Abstract

The turning operation is a traditional and conventional process for machining cylindrical materials to achieve a desired shape. During machining in the turning process, the accuracy and quality of the end product mainly depend on its process parameters. Therefore, this study attempts to achieve a quality product from a conventional lathe machine by optimizing its process parameters for En2-BS970 mild steel. The experiments were designed with the help of response surface methodology (RSM). The most influential turning parameters, such as feed rate, spindle speed, cutting fluid flow rate, and cutting angle, are investigated experimentally. In addition, the machining responses, namely material removal rate (MRR), surface roughness (SR), cutting force (CF), and cutting time (CT), have been optimized using the RSM numerical optimization method. Furthermore, a deep neural network (DNN) machine learning prediction model based on the whale optimization algorithm (WOA) is developed for this experiment in order to predict turning performances for En2-BS970 material. The predicted DNN results showed an accuracy of about 90% compared to experimental results, indicating that the implementation of WOA significantly optimized the DNN weights during training. The RSM optimized responses are obtained as 14.608 mm3/min for MRR, 0.7504 µm for SR, 442.94 N for CF, and 2.48 seconds for CT during the turning operation with input settings of 0.24 mm/rev, 466.535 rpm, 0.075 ml/s and 60.18° for feed rate, spindle speed, cutting fluid flow rate and cutting edge angle respectively.