Computational Modelling of Additive Manufacturing

Inbunden, Engelska, 2026

AvWentao Yan,Yanming Zhang,Daijun Hu,Yuhan Ju,Yiming Liu

1 939 kr

Kommande

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 processesMulti-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 evolutionsMulti-scale modeling of thermal and residual stresses, and the room- and high-temperature mechanical properties of the additively manufactured partsData-driven surrogate modeling, uncertainty quantification and optimization, and the synergy with digital twins, smart diagnostics, and controlThis 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.

Produktinformation

Utgivningsdatum2026-11-02
FormatInbunden
SpråkEngelska
Antal sidor496
FörlagJohn Wiley & Sons Inc
ISBN9781394351022

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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.

Preface xiAcknowledgments xvAcronyms xvii1 Introduction 12 Modeling of powder dynamics 52.1 Introduction 52.2 Methodology 62.3 Experimental validation 142.4 Modeling of powder spreading in powder-bed-based additive manufacturing 162.5 Modeling of powder feeding in powder-feed directed energy deposition 282.6 Modeling of spattering in laser powder bed fusion 332.7 Summary 393 Modeling of melt pool dynamics 433.1 Introduction 433.2 Methodology 463.3 Simulation of single-material PBF process 923.4 Simulation of multi-material PBF process 1243.5 Simulation of AM process under external fields 1593.6 Summary 1804 Modeling of micro-structure evolutions 1814.1 Introduction 1814.2 Modeling of grain evolutions 1824.3 Modeling of dendrite evolutions 1994.4 Modeling of precipitation 2314.5 Summary 2475 Modeling of thermal/residual stresses 2495.1 Introduction 2495.2 Experimental validation 2535.3 Micro-scale model of thermal/residual stresses 2555.4 Meso-scale model of thermal/residual stresses 2785.5 Macro-scale model of thermal/residual stresses 2925.6 Model of dislocation evolution driven by thermal stresses 3025.7 Model of dynamic recrystallization driven by thermal stresses 3175.8 Summary 3246 Modeling of mechanical properties of AM materials 3276.1 Introduction 3276.2 Methodology 3296.3 Modeling of mechanical properties of AM AlSi10Mg: room- and high-temperature 3316.4 Brief summary 3436.5 Strain localization in additively manufactured material 3446.6 Fracture and fatigue modeling in AM materials 3646.7 Summary 3777 Data-driven Modelling and Analysis 3797.1 Introduction 3797.2 Data Foundations and Modelling Workflow for AM 3807.3 Statistical Methods for Data-driven Analysis 3817.4 Data-driven Surrogate Modelling 3887.5 Uncertainty Quantification 4217.6 Summary 4318 Synergy of Modelling with Other Techniques 4338.1 Introduction 4338.2 Data Collection 4358.3 Data Handling and Preprocessing 4398.4 Digital Twin for AM 4518.5 Towards Closed-Loop Control 4558.6 Summary 460