Deep Learning-Based Inverse Identification of the Johnson–Cook Yield and Fracture Parameters for Ti-6Al-4V Fabricated by Laser Powder Bed Fusion Using Batch Finite Element Simulation Data

Finite element analysis (FEA) is a key tool for predicting the mechanical behavior of additively manufactured parts. However, its predictive accuracy relies on the underlying constitutive model and, in particular, on precise model parameters. The main purpose of this study is to develop and validate a condition-grouped multimodal deep learning framework for efficiently identifying the ten parameters of the Johnson–Cook (JC) yield and fracture models for Ti-6Al-4V (TC4) alloy fabricated by laser powder bed fusion (L-PBF) using batch finite element simulation data. Using nearly 20,000 simulation datasets and response images covering different temperatures, strain rates, and stress triaxialities, convolutional neural networks (CNNs), long short-term memory (LSTM), and multilayer perceptron (MLP) models are trained to capture both sequential and physical features of the stress–strain responses. Grouping the data by test condition decouples the coupled model parameters and improves prediction accuracy. The identified yield parameters give R2 values consistently above 0.94, and the stress–strain curves reconstructed from the identified parameters agree well with experimental data (R = 0.976). The failure parameters reproduce the main fracture characteristics in simulation, but the post-peak behavior remains subject to uncertainty. The proposed method therefore allows the corresponding JC parameters to be determined directly from stress–strain curves, providing a fast route for calibrating constitutive models of TC4 components.