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.