Optimizing process parameters for hot forging of Ti-6242 alloy: A machine learning and FEM simulation approach

Abstract

In this study, we investigated the hot deformation behavior of Ti–6Al–2Sn–4Zr–2Mo (Ti-6242) alloy and propose a method to derive optimal hot process parameters for grain refinement and avoidance of flow instability. Microstructural Risk Index (MRI) was introduced as a microstructural evaluation index consisting of grain size, standard deviation of grain size, and flow instability. The initial temperature of the material and the stroke speed of the die were selected as design variables. Finite element analysis (FEA) was used to calculate grain size and flow instability and determine MRI of the forgings. The grain size model coefficient and flow instability were calculated based on the flow stress curve and verified with optical microscope (OM) and electron backscatter diffraction (EBSD) analysis results. The Deep Neural Network (DNN) model was used to determine the optimal process parameters for the forging process. The MRI prediction accuracy of the trained DNN models showed excellent performance at 97.01 %. The MRI of the optimized process variables was improved by 7.95 % compared to the minimum MRI of the training data set. Optimized process parameters can improve the quality of forgings through grain refinement and avoidance of flow instability regions.