Laser powder bed fusion (LPBF) is widely used in metal additive manufacturing to create geometrically complex parts, where heat transfer and its affected temperature distribution significantly influence the parts’ materials’ microstructure and the resulting materials’ properties. Among all the microstructure representations, crystallographic orientations play a paramount role in determining the mechanical properties of materials. This paper first developed a physics-based analytical model to predict the 3D temperature distribution in PBF considering heat transfer boundary conditions; heat input using point-moving heat source solutions; and heat loss due to heat conduction, convection, and radiation. The superposition principle obtained temperature distributions based on linear heat sources and linear heat loss solutions. Then, the temperature distribution was used to analytically obtain the texture grown on a substrate with random grain orientations considering columnar-to-equiaxed transition (CET). Thus, the link between process parameters and texture was established through CET models and physical rules. Ti-6Al-4V was selected to demonstrate the capability of the analytical model in a single-phase situation. By applying advanced thermal models, the accuracy of the texture prediction was evaluated based on a comparison of experimental data from the literature and past analytical model results. Hence, this work not only provides a method of the fast analytical simulation of texture prediction in the single-phase mode for metallic materials but also paves the road for subsequent studies on microstructure-affected or texture-affected materials’ properties for both academic research and industrial applications. The prediction of single-phase material texture has never been achieved before, and the scalability has been expanded.

Additive Manufacturing (AM) has become a revolutionary technology in manufacturing, attracting considerable attention in industrial applications recently. It allows for intricate fabrication, reduces material waste, offers design flexibility, and has economic implications. Nonetheless, the residual stresses generated during the AM process and their effects on microstructural evolution and material properties continue to pose significant challenges hindering its advancement. This paper investigates the evolution of microstructures, focusing on texture and grain size as influenced by processing parameters. It examines how these factors affect the performance of multi-phase materials, specifically in terms of elastic modulus, Poisson’s ratio, and yield strength, leading to variations in residual stress through analytical simulation. The authors developed a thermal model that considers heat transfer boundaries and the geometry of the molten pool. They simulated grain size by considering the heating and cooling processes, including thermal stress, the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model, and grain refinement. The texture was simulated using the Columnar-to-Equiaxed Transition (CET) model, thermal dynamics, and Bunge calculations. The self-consistency model determines the properties based on the established texture distribution. Finally, both microstructure-affected and non-affected residual stresses were modeled and compared. Two gaps between microstructure-affected residual stress and non-affected analytical models appear at the depths of 0.02 mm and 0.078 mm. The results indicate that controlling process parameters and optimizing microstructures can effectively reduce residual stresses, significantly enhancing the overall performance of AM components. Hence, this work provides a more accurate analytical residual stress model and lays the foundation for better control of residual stress in the AM industry.

For advancing manufacturing, arising AM, with an inverse philosophical approach compared to conventional procedures, has benefits that include intricate fabrication, reduced material waste, flexible design, and more. Regardless of its potential, AM must overcome several challenges due to multi-physical processes with miscellaneous physical stimuli in diverse materials systems and situations, such as anisotropic microstructure and mechanical properties, a restricted choice of materials, defects, and high cost. Unlike conventional experimental work that requires extensive trial and error resources and FEM, which generally consumes substantial computational power, the analytical approach based on physics is an exceptional choice. Understanding the relationship between the microstructure and material properties of the fabricated parts is a crucial focus in AM research. Texture is a vital factor in almost every modern industry. This study first proposed a physics-based model to foreshadow the multi-phase crystallographic orientation distribution in Ti-6Al-4V LPBF while considering the part boundary conditions due to the importance of part geometry in real industry. The thermal distribution obtained from this function operates as the information for the single-phase crystallographic texture model. In this model, we forerun and validate the orientations of single-phase materials utilizing three Euler Angles with the principles of CET and thermodynamics, as well as the intensity of the texture by approximating them with published results. Then, we transform the single-phase texture into a dual-phase texture in Bunge calculation, illustrating visualized by pole figures of both BCC and HCP phases. The tendency and appearances of both BCC and HCP phases in pole figures predicted agree well with the experimental results. This texture evolution model provides a new paradigm for future researchers to model the texture or microstructure evolution semi-analytically and save many computational resources in a real-world perspective. Others have not yet done this work about simulating the multi-phase texture in an analytical approach, so this work bridges the gap in this field. Furthermore, this paper establishes the foundation for future research on materials properties affected by microstructure or texture in academic and industrial environments. The precision and dependability of the results obtained through this method make it a valuable tool for ongoing research and advancement.

Additive manufacturing has been broadly employed to create geometrically complex parts, where processing parameters significantly influence the resulting parts’ materials microstructure and properties. However, additive manufacturing still faces limitations such as anisotropic microstructure and mechanical properties, restrictions on material selection, defects, and high cost. Grain size, usually quantitatively represented by mean grain diameter, is a vital microstructure feature closely linked to strength properties. An accurate physics-based analytical model is yet to be developed fully for process-structure–property prediction. In this work, the authors first develop the thermal model, considering heat transfer boundary conditions and molten pool geometry. Then, the grain size is simulated with both the heating and cooling processes considered, including thermal stress consideration, Johnson-Mehl-Avrami-Kolmogorov kinetics, and grain refinement. The microstructure-affected properties are also included to improve this analytical grain size model. In particular, the texture is first simulated using the columnar-to-equiaxed transition model, thermal dynamics, and Bunge calculation. After establishing the texture distribution, the visco-plastic self-consistency model acquires the properties of the affected materials. Then, the updated properties are embedded into the grain size model for greater accuracy. The Ti-6Al-4V alloy was chosen to showcase the effectiveness of analytical models in a multi-phase scenario. By using advanced models, prediction accuracy was significantly enhanced. Validation against experimental data demonstrated a mean agreement of 93.03% for grain size prediction across scanning speeds, reaching 97.01% at the industrially relevant 600 mm/s. Compared to prior analytical models, the developed framework achieved a mean accuracy improvement of 23.8%, with a peak improvement of 34.6% at 600 mm/s. The precision and dependability of the results obtained through this approach make it a valuable tool for future research and industrial development. This work provides a novel framework for improving the modeling of the microstructure features of materials more accurately while taking evolving property values into account, in addition to a sound analytical method for modeling grain size in additive manufacturing.

Advanced manufacturing focuses on intelligence, networking, and digitalization within the global decarbonization and sustainable production agreement. Additive Manufacturing (AM) has become a revolutionary technology in manufacturing, attracting considerable attention in industrial applications recently. It allows for intricate fabrication, reduces material waste, offers design flexibility, and has economic implications. Nonetheless, the residual stresses generated during the AM process and their effects on microstructural evolution and material properties continue to pose significant challenges hindering its advancement. This paper investigates the evolution of microstructures, focusing on texture and grain size as influenced by processing parameters. It examines how these factors affect the performance of multi-phase materials, specifically in terms of elastic modulus, Poisson’s ratio, and yield strength, leading to variations in residual stress through analytical simulation. The authors developed a thermal model that considers heat transfer boundaries and the geometry of the molten pool. They simulated grain size by considering the heating and cooling processes, including thermal stress, the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model, and grain refinement. The texture was simulated using the Columnar-to-Equiaxed Transition (CET) model, thermal dynamics, and Bunge calculations. The self-consistency model determines the properties based on the established texture distribution. Finally, both microstructure-affected and non-affected residual stresses were modeled and compared. The results indicate that controlling process parameters and optimizing microstructures can effectively reduce residual stresses, significantly enhancing the overall performance of AM components.