Development of a Physics-Based 3D Microstructure Prediction Capability for Metal Additive Manufacturing

Metal additive manufacturing (AM) holds tremendous promise for revolutionizing various industries by enabling the production of complex geometries and customized components. However, understanding and controlling the material properties of additively manufactured parts remain challenging. The microstructure, which is influenced by process parameters, plays a crucial role in determining the final material properties. Characterizing and experimentally identifying the microstructure of AM parts is often tedious, time-consuming, and expensive. Therefore, the need for simulation tools to predict microstructures becomes imperative. In this study, we have developed a physics-based 3D microstructure prediction capability using cellular automata (CA) method for metal AM. Our approach focuses on coupling thermal and microstructure solvers to accurately predict the evolution of the microstructure during the AM process. By capturing the intricate interplay between heat transfer, phase transformations, and solidification, our solver offers a comprehensive understanding of microstructure formation in AM parts. The preliminary results obtained from our developed solver have shown great promise. We have observed that the process parameters significantly affect the materials' microstructure, which in turn controls the mechanical properties. Our simulation tool allows us to explore various process conditions and optimize them to achieve desired material properties, leading to improved part performance. To ensure efficient computations, we have parallelized the solver, enabling fast and accurate predictions. This capability provides a valuable tool for researchers, engineers, and manufacturers in the metal AM industry to optimize process parameters, reduce trial-and-error experimentation, and accelerate product development. Overall, our developed physics-based 3D microstructure prediction capability offers a cost-effective and time-efficient alternative to characterization and experimental identification methods. By leveraging simulation, we can gain valuable insights into the complex interplay between process parameters, materials, and microstructure, ultimately advancing the field of metal additive manufacturing and unlocking its full potential for various applications.