A mechanism-data fusion framework for process-microstructure prediction and optimization in laser directed energy deposition of Ti-6Al-4V using dual-level surrogate modeling
TL;DR
A physics-informed mechanism-data fusion framework that couples macroscopic finite element method (FEM) and microscopic phase-field method (PFM) with a dual-level long short-term memory (LSTM) neural network with a dual-level data-driven surrogate model is developed.
Abstract
laser-directed energy deposition (L-DED) process involves complex thermal and material interactions, posing challenges for controlling microstructural evolution in metal additive manufacturing. To enable rapid, efficient process optimization, this study develops a physics-informed mechanism-data fusion framework that couples macroscopic finite element method (FEM) and microscopic phase-field method (PFM) with a dual-level long short-term memory (LSTM) neural network. A multiscale physical model is employed to investigate grain growth behavior and the influence of process parameters on microstructural morphology. To map this non-linear temporal evolution, a dual-level data-driven surrogate model is developed. Compared to a conventional multilayer perceptron (MLP), the LSTM effectively captures time-dependent thermal accumulation. In recursive predictions, four initial input layers were identified as the optimal initialization length, enabling stable