- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
Understand a new set of tools for incorporating additive manufacturing into design
Additive Manufacturing, also known as 3D printing, is a method for building three-dimensional objects by layering materials. It has revolutionized every area of the manufacturing industry, permitting the rapid and precise fabrication of complex objects constructed of heterogeneous materials and with varied applications. The race is now on for design principles and technologies to keep pace with this explosive new manufacturing market.
Design for Additive Manufacturing responds to this demand for new design methods with a thorough and cutting-edge overview. Beginning with fundamental principles of additive manufacturing, the book analyzes how decisions made at the design stage will impact the final product, before building out a comprehensive framework for understanding the interplay between additive manufacturing and design. The result is a must-have resource for students, researchers, and industry professionals alike.
Design for Additive Manufacturing readers will also find:
Tools for quickly identifying the most suitable design approach by which to incorporate additive manufacturing into a given project
Detailed coverage of the latest trends in design for additive manufacturing, including data-driven design
Examples and case studies highlighting different AM design methods
Design for Additive Manufacturing is ideal for students and instructors in additive manufacturing courses, as well as mechanical engineers, designers, and researchers working with additive manufacturing elements.
Contents
Preface vii
Acknowledgments ix
List of Abbreviations xi
1 Introduction 1
1.1 Additive Manufacturing 1
1.2 Design for Additive Manufacturing (DfAM) 7
1.3 Challenges and Future Directions of DfAM 9
References 11
2 Engineering Design Process 15
2.1 Design Function Analysis 16
2.2 Concept Generation 17
2.3 Geometric Modeling 18
2.4 Detailed Design 19
2.5 Computational Design 20
2.6 Bridging the Gap: From Traditional Design to Design for Additive Manufacturing 22
References 23
3 Design Conceptualization with Additive Manufacturing 25
3.1 Introduction 25
3.2 DfAM Worksheet 26
3.3 Design Heuristics for AM 28
3.4 Integrated DfAM Method 30
3.5 Automated AM Candidacy Prediction Tools 34
3.6 Multifunctional Bio-inspired Design Method 40
3.7 Summary 40
References 41
4 Part Consolidation and Assembly Design 45
4.1 What Is Part Consolidation? 45
4.2 Literature Review 48
4.3 The Holistic Part Consolidation Design Workflow 52
4.4 Part Candidate Selection 54
4.5 Fusion of Parts 66
4.6 Emerging Trends in Part Consolidation and Assemblies 80
4.7 Summary 84
References 84
5 Manufacturing Constraints and Design Guides for Additive Manufacturing 89
5.1 General Constraints and Guidelines for AM 90
5.2 Design Guidelines for Polymeric AM Process 96
5.3 Design Guidelines for Metal AM Process 105
5.4 Design Guidelines for Other AM Processes 110
5.5 Conclusions 113
References 114
6 Topology Optimization, Structural Optimization, and Multifunctional Optimization 117
6.1 Basic Concepts of Engineering Optimization 117
6.2 Structural and Topology Optimization for AM 122
6.3 Multi-disciplinary Design Optimization for AM 128
6.4 Reliability-based Design Optimization 130
6.5 Conclusion 131
References 131
7 Lattice Structure Design and Simulation 133
7.1 Introduction to Lattice Structures 133
7.2 Design Methodology for Lattice Structures 136
7.3 Numerical Simulation of Lattice Structures 141
7.4 Lattice Structure Optimization Approaches 145
7.5 Design Cases of Lattice Structures 149
7.6 Bio-inspired Lattice Design 153
7.7 Conclusion 154
References 154
8 Advanced Topics in DfAM: Digital Materials and Emerging Paradigms 157
8.1 Introduction to Digital Materials 157
8.2 Fabrication of Digital Materials 158
8.3 Multi-materials Design 161
8.4 Voxel Printing Design 164
8.5 Fiber-reinforced Composite Design 165
8.6 Summary 167
References 167
9 Data-driven and Machine Learning-based Design for Additive Manufacturing 171
9.1 Foundations of Data-driven and ML-based DfAM 172
9.2 ML Across DfAM Workflow 173
9.3 Cross-cutting Strategies and Future Directions 176
References 178
Index 179
