Predictive Modelling Framework on the Basis of Artificial Neural Network: A Case of Nano-Powder Mixed Electric Discharge Machining

In this modern era where Industry 4.0, plays a crucial role in enhancing productivity, quality, and resource utilization by digitalizing and providing smart operation to industrial systems. Therefore, there is a need to establish a framework that enhances productivity and quality of work to achieve the net-zero from industry. In this study, a comprehensive and generic analytical framework has been established to mitigate or lessen the research and technological gap in the manufacturing sector. In addition to that, the key stages involved in artificial intelligence (AI) based modelling and optimization analysis for manufacturing systems have also been incorporated. To assess the proposed AI framework, electric discharge machining (EDM) as a case study has been selected. The focus enlightens the emergence of optimizing the material removal rate (MRR) and surface roughness (SR) for Inconel 617 (IN617) material. A full factorial design of the experiment was carried out for experimentation. After that, an artificial neural network (ANN) as a modelling framework is selected, and fine-tuning of hyperparameters during training has been carried out. To validate the predictive performance of the trained models, an external validation (Valext) test has been conducted. Through sensitivity analysis (SA) on the developed AI framework, the most influential factors affecting MRR and SR in EDM have been identified. Specifically, powder concentration (Cp) contributes the most to the percentage significance, accounting for 79.00 % towards MRR, followed by treatment (16.35 %) and 4.67 % surfactant concentration (Sc). However, the highest % significance in SR is given by Sc (36.86 %), followed by Cp (33.23 %), and then treatment (29.90 %), respectively. Furthermore, a parametric optimization has been performed using the framework and found that MRR and SR are 93.75 % and 58.90 % better than experimental data. This successful performance optimization proposed by the framework has the potential for application to other manufacturing systems.