The 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 prediction and mitigating potential layer-wise error propagation, maintaining average R2 values of around 0.95. Using the average grain area as the primary microstructural control target, and incorporating the average grain width, a parameter range of 500–550 W and 13–14 mm/s was identified for achieving near-equiaxed β grains. The proposed framework is implemented as a modular computational pipeline that integrates finite element, phase-field, and surrogate models for reusable and extensible process-microstructure prediction in metal additive manufacturing.

Predicting the evolution of microstructure and field quantities under varying processing and loading conditions is a central challenge in computational materials science and metal additive manufacturing (AM). While deep learning (DL) methods offer ultra-fast prediction capabilities post-training, existing models often struggle with poor spatial and temporal extrapolation, high parameter burdens, and an inability to effectively integrate diverse conditioning parameters alongside high-dimensional input fields. To address these bottlenecks, we propose a novel conditional generative adversarial network (CPGAN), which is designed to seamlessly ingest both initial fields and governing condition parameters. The CPGAN framework offers three distinct advantages: (1) it accurately maps the combined effects of initial states and process conditions onto evolved fields; (2) it demonstrates robust extrapolation capabilities across diverse spatial and temporal scales, including the unique ability to natively generate high-resolution rectangular domains; and (3) it achieves superior predictive accuracy and training stability compared to standard convolutional baselines by effectively suppressing spurious artifacts. We validate CPGAN’s performance against rigorous physics-based ground truths across three representative engineering applications: porosity evolution in selective laser sintering (SLS), spatial distribution of 2D von Mises stress fields in solid structures, and the spatiotemporal evolution of grain growth. The results confirm that CPGAN is a highly adaptable and efficient surrogate model, capable of simulating continuous structural and morphological evolutions even when driven by highly non-uniform spatial or temporal kinetics.