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Contents
Dedication
Preface
Acknowledgements
List of Abbreviations
About the Authors
Chapter 1 - Introduction
Additive Manufacturing
Design for Additive Manufacturing (DfAM)
Challenges and Future Directions of DfAM
Chapter 2 - Engineering Design Process
Design Function Analysis
Concept Generation
Geometric Modeling
Detailed Design
Computational Design
Bridging the Gap: From Traditional Design to Design for Additive Manufacturing
Chapter 3 - Design Conceptualization with Additive Manufacturing
Introduction
DfAM Worksheet
Design Heuristic Cards
Integrated DfAM Method
Automated AM Candidacy Prediction Tools
Multifunctional Bio-inspired Design Method
Chapter 4 - Part Consolidation and Assembly-Level DfAM
What is Part Consolidation?
Literature Review
The Holistic Part Consolidation Design Workflow
Part Candidate Selection
Functional-Entity-Based Fusion
Emerging Trends in Part Consolidation and Assembly-free Design
Chapter 5 - Manufacturing Constraints and Design Guides for Additive Manufacturing
General Constraints and Guidelines for AM
Design Guidelines for Polymeric AM process
Design Guidelines for metal AM process
Design Guidelines for other AM process
Conclusions
Chapter 6 - Topology optimization, structural optimization and multi-functional optimization
Basic concepts of engineering optimization
Structural and topology optimization for AM
Multi-disciplinary Design Optimization for AM
Reliability-based design optimization
Conclusions
Chapter 7 - Lattice structure design and simulation
Introduction to lattice structures
Design methodology for lattice structures
Numerical simulation of lattice structures
Lattice structure optimization approaches
Design cases of lattice structures
Bio-inspired lattice design
Conclusion
Chapter 8 - Advanced Topics in DfAM: Digital Materials and Emerging Paradigms
Introduction to digital materials
Fabrication of digital materials
Multi-materials design
Voxel printing design
Fiber-reinforced composite design
Summary
Chapter 9 - Data-Driven and Machine Learning-Based Design for Additive Manufacturing
Foundations of Data-Driven and Machine Learning-Based DfAM
ML across DfAM workflow
Cross-Cutting Strategies and Future Directions
Index
