Additive Manufacturing (AM) is an emerging technology enabling the manufacturing of highly complex geometries with location-specific properties for both structural and functional materials, including but not limited to metals, plastics, and ceramics. It has been rapidly shifting from prototype to mass production in recent years. During fabrication, a wide range of processing parameters can be modified that contribute to the integrity and properties of the final product. AM materials also experience unique thermal history with cyclic heating and cooling, which yields dramatically different materials behaviors compared to those of conventional manufacturing techniques, such as casting and welding. Computational modeling that targets complex physical phenomena involved in AM has been evolved to assist the development of both AM processes and materials. The latest advancements have been resolving the challenges in multiple perspectives of various AM processes for different materials systems, including innovation of machine architecture, new topological design, process optimization for defect minimization/elimination and microstructural customization, and novel materials discovery.
The multiphysics and multiscale nature of AM processes still poses significant challenges to reveal the physical phenomena occurring during and after the fabrication process. Computational models span several length scales, ranging from macroscale models for thermal stress and materials properties, mesoscale models for powder spreading/deposition, sintering and/or melt pool dynamics, and microscale models for microstructure and defects. However, most of these models have been independently developed or interdependently coupled to provide an in-depth understanding of the process itself and the behaviors of the resultant material, enabling the prediction of process-microstructure-property relationships. Novel experimental techniques, such as dynamic transmission electron microscope, in situ X-ray imaging and diffraction, and high-speed thermal imaging, have also been developed to reveal the previously inaccessible dynamics, providing data to support theoretical predictions and validating models at different temporal and spatial scales.
The aim of this Research Topic is to solicit state-of-the-art developments in the broad field of computational modeling for AM. Areas covered in this Research Topic include, but are not limited to:
• Materials of metal, plastics, and ceramics
• Advanced multi-scale computational methods
• New modeling capabilities
• High-performance computing
• Integrated computational materials engineering
• Model-based process optimization
• Applications of artificial intelligence
• Microstructure and defects
• Recrystallization and phase transformation
• Combined experimental and numerical studies
Additive Manufacturing (AM) is an emerging technology enabling the manufacturing of highly complex geometries with location-specific properties for both structural and functional materials, including but not limited to metals, plastics, and ceramics. It has been rapidly shifting from prototype to mass production in recent years. During fabrication, a wide range of processing parameters can be modified that contribute to the integrity and properties of the final product. AM materials also experience unique thermal history with cyclic heating and cooling, which yields dramatically different materials behaviors compared to those of conventional manufacturing techniques, such as casting and welding. Computational modeling that targets complex physical phenomena involved in AM has been evolved to assist the development of both AM processes and materials. The latest advancements have been resolving the challenges in multiple perspectives of various AM processes for different materials systems, including innovation of machine architecture, new topological design, process optimization for defect minimization/elimination and microstructural customization, and novel materials discovery.
The multiphysics and multiscale nature of AM processes still poses significant challenges to reveal the physical phenomena occurring during and after the fabrication process. Computational models span several length scales, ranging from macroscale models for thermal stress and materials properties, mesoscale models for powder spreading/deposition, sintering and/or melt pool dynamics, and microscale models for microstructure and defects. However, most of these models have been independently developed or interdependently coupled to provide an in-depth understanding of the process itself and the behaviors of the resultant material, enabling the prediction of process-microstructure-property relationships. Novel experimental techniques, such as dynamic transmission electron microscope, in situ X-ray imaging and diffraction, and high-speed thermal imaging, have also been developed to reveal the previously inaccessible dynamics, providing data to support theoretical predictions and validating models at different temporal and spatial scales.
The aim of this Research Topic is to solicit state-of-the-art developments in the broad field of computational modeling for AM. Areas covered in this Research Topic include, but are not limited to:
• Materials of metal, plastics, and ceramics
• Advanced multi-scale computational methods
• New modeling capabilities
• High-performance computing
• Integrated computational materials engineering
• Model-based process optimization
• Applications of artificial intelligence
• Microstructure and defects
• Recrystallization and phase transformation
• Combined experimental and numerical studies
