Efficient Model-Based Framework for Microstructure Design in LPBF of Ti-6Al-4V

The microstructure of Ti-6Al-4V plays a pivotal role in determining its mechanical and fatigue properties. However, the microstructure evolution during the Laser Powder Bed Fusion (LPBF) process depends critically on the thermal history of the process, which is inherently complex and cyclic in nature. Traditionally, favorable α+β microstructures are achieved by decomposing the as-built α’-martensite through post-process heat treatments, or through more recently investigated in-situ heat treatments. While both of these methods introduce additional process steps and complexity, this work focuses on leveraging LPBF process parameter tuning to directly tailor the microstructure. The key challenge lies in navigating the vast and multidimensional design space of process parameters in LPBF, compounded by the scarcity of operando synchrotron data and hence reliance on mostly post-mortem microstructural analysis. To address this, we present a model-based framework that couples a computationally efficient thermal model with a microstructure evolution model including stable αs-phase, martensitic αm-phase and β-phase. This framework predicts microstructure evolutions across various parameter combinations, validated against experimental data. Our approach enables rapid screening of thousands of parameter combinations—including volumetric energy density, layer thickness, interlayer time, and build plate temperature—at ~1000× faster than the scan-resolved models used in literature, yet with sufficient accuracy. This allows us to map attainable microstructures, identify optimal parameters, and solve practical challenges like preserving a favorable αs+β structure under varying interlayer times or spatially tailoring microstructures while controlling αs lath thickness. Since traditional forward exploration becomes increasingly tedious and computationally expensive when varying process parameters layer-wise, we also explore gradient-based inverse design to determine layer-specific parameters for targeted microstructures. This work marks a step toward fully utilizing the LPBF process window for microstructure design by thermal-microstructural modeling and optimization, thereby minimizing trial-and-error and enabling accelerated development of engineered microstructures.