Computational Modelling of Additive Manufacturing

Enables readers to understand the additive manufacturing process-structure-property relationships and optimize the manufacturing parameters and machines via computational modeling techniques, including multi-physics modeling and data-driven analysis.

Computational Modeling of Additive Manufacturing provides a step-by-step guide, covering topics from basic physics and governing equations to the implementation, experimental validation, model capability, and physical mechanisms obtained from simulation, as well as expert perspectives on future outlooks. With a well-rounded exploration of key areas, this book integrates various modeling techniques, including multi-physics modeling of powder, fluid and solid mechanics, heat transfer, microstructure evolutions, data-driven analysis including surrogate modeling, uncertainty quantification and multi-objective optimization, and a synergy of modeling with other techniques. Modeling of various additive manufacturing processes are included, such as laser/electron beam powder bed fusion, directed energy deposition, binder jetting, and material extrusion.

The book presents comprehensive simulations covering a variety of issues for the process-structure-property relationships, including:

• Multi-phase flow modeling of powder dynamics (e.g., spreading and spattering) in powder-bed-based additive manufacturing processes
• Multi-physics modeling of molten pool dynamics in fusion-based additive manufacturing processes, with mechanistic heat source model, composition-dependent evaporation and chemical reaction models, and under external energy fields, as well as the thermal-fluid dynamics in other additive manufacturing processes.
• Modeling of microstructure evolutions including grain and dendrite evolutions
• Multi-scale modeling of thermal and residual stresses, and the room- and high-temperature mechanical properties of the additively manufactured parts
• Data-driven surrogate modeling, uncertainty quantification and optimization, and the synergy with digital twins, smart diagnostics, and control

This book is an essential first-of-its-kind reference for researchers in this specific area, and the larger area, of additive manufacturing who focus on experiments, materials design, mechanics, and other areas of interest.

Wentao Yan, PhD, is a Tenured Associate Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS). Since 2014, he has been working on multi-scale multi-physics modeling, experimental investigation and data analysis of additive manufacturing. He has published >140 papers on flagship journals and his team was the winner in the 2022 NIST AM Bench Simulation Challenges, winning 9 awards in 25 tests.

Yanming Zhang, PhD, is a Research Fellow in the Department of Mechanical Engineering at NUS. His research focuses on the multiphase flow modeling of multi-material additive manufacturing.

Daijun Hu, PhD, is a Research Fellow in the Department of Mechanical Engineering at NUS. His research focuses on residual stress, dislocation dynamics and fracture, providing mechanistic insights into material behavior through computational modeling of additive manufacturing.

Yuhan Ju is a PhD candidate in the Department of Mechanical Engineering at NUS. Her research focuses on the modeling of microstructure evolutions in additive manufacturing at grain and dendrite scales with the phase-field and cellular automaton methods.

Yiming Liu is a PhD student under the Integrative Sciences and Engineering Programme at NUS. His research focuses on data-driven modeling, diagnostics and close-loop control in additive manufacturing.