Hybrid MLP-PSO-based Technique to Predict Process Parameters and Alloying Compositions in ADI for Sustainable Manufacturing

Abstract

Due to some of the attractive physical properties, e.g., high ductility and strength, and machinability, austempered ductile iron (ADI) has been widely used to produce components, in heavy industries, automobiles, farm machineries, and railways. Depending on the design requirements, a component requires ADI with specific physical characteristics. Several alloying elements are added to molten iron and then it undergoes a complex austempering thermal process to produce ADI. During the austempering process, the alloying elements interact with iron in a highly nonlinear complex manner that leads to formation of microstructure which determines its physical properties. However, currently, there is no technique available that can specify the austempering temperature and time, and alloying element proportions to produce ADI with a specific characteristic. In this paper, we propose a novel hybrid multilayer perceptron (MLP) and particle swarm optimization (PSO)-based technique to predict the austempering process parameters and alloying compositions to produce ADI with a specific physical characteristic, e.g., Vickers hardness number (VHN). In the first phase, an MLP is trained to learn the austempering process in a forward modeling scheme using the experimental data taken from literature. In the second phase, in an inverse modeling scheme, using the trained MLP and PSO algorithm, a solution is obtained that provides the predicted austempered process parameters and alloying elements to produce ADI with a specific VHN. With extensive simulation results it is shown that the proposed technique can provide feasible and accurate solutions that provide optimum use of expensive alloying materials leading to sustainable manufacturing.