Investigations on Microstructure Evolution of Metal Additive Manufacturing by Experiments, Physics-based and Data-driven Modeling

Abstract

Metal additive manufacturing (AM) involves a suite of processes where raw metal materials are joined or solidified under computer control to create three-dimensional objects. The AM process facilitates streamlined product development cycles, maximizes the potential of computer-aided design (CAD) to foster designers' creativity, and enables the fabrication of intricate, on-demand parts. AM has found applications in diverse fields, ranging from biomedical to aerospace, and even the oil and gas industry. However, its complexity and the array of process factors involved pose significant challenges in comprehensively understanding and optimizing microstructure evolution, a critical aspect for quality control and performance enhancement. Despite the considerable research efforts dedicated to studying the intricate AM process-microstructure relationship, such as grain structures, porosity, and phase, numerous questions about microstructural development persist. This is primarily due to the complexity of the AM process and the variety of metal species involved. Key questions include: (1) How do dynamic thermal conditions influence microstructural development, particularly in the context of complex physics during AM of multi-phase metals (e.g., Ti-6Al-4V)? (2) How does the potential flow-driven compositional redistribution affect the microstructural development of bimetals? (3) How can we employ cost-effective and swift data-driven modeling to expedite the establishment of complex AM process-microstructure relationships for microstructure optimization and quality control? To effectively address these questions, we employed a physics-based framework that includes experimental fabrication and validation, physics-based additive manufacturing modeling, and data-driven modeling to deepen our understanding of microstructural development. This research provides insights into microstructural evolution during AM of single metals and bimetals and proposes a cost-effective and efficient method for microstructural optimization through melt pool control. Specifically, we thoroughly examined the gradient structure featured with different phases in AM-fabricated Ti-6Al-4V, and systematically explored and validated the location-dependent phase evolution. Additionally, we achieved a quantitative understanding of the bimetallic structures of SS316L and IN625 by directed energy deposition (DED). Finally, we established a data-driven modeling framework with experimental data inputs from the National Institute of Standards and Technology (NIST) for rapid extrapolative melt pool prediction for unbuilt parts. This study presents a physics-based framework allowing for a comprehensive understanding of microstructural evolution, with the goal of achieving microstructure optimization and quality control of AM products.