This book is a comprehensive guide to the latest developments in data-driven additive manufacturing (AM). From data mining and pre-processing to signal processing, computer vision, and more, the book covers all the essential techniques for preparing AM data. Readers willl explore the key physical and synthetic sources of AM data throughout the life cycle of the process and learn about feature engineering techniques, pipelines, and resulting features, as well as their applications at each life cycle phase. With a focus on featurization efforts from reviewed literature, this book offers tabular summaries for major data sources and analyzes feature spaces at the design, process, and structure phases of AM to uncover trends and insights specific to feature engineering techniques. Finally, the book discusses current challenges and future directions, including AI/ML/DL readiness of AM data.
Whether you're an expert or newcomer to the field, this book provides a broader summary of the status and future of data-driven AM technology.
Author(s): Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao
Series: SpringerBriefs in Applied Sciences and Technology
Publisher: Springer
Year: 2023
Language: English
Pages: 150
City: Cham
Acknowledgments
Contents
About the Authors
Abbreviations
1 Introduction
1.1 Additive Manufacturing: Motivation, Challenges, and Potential Solutions
1.2 Status of Data-Driven Additive Manufacturing
1.3 Why Feature Engineering?
1.4 Review Specifics
References
2 Feature Engineering in Additive Manufacturing
2.1 Domains and Paradigms
2.2 Feature Sources
2.3 Feature Engineering Techniques
2.4 Generic Data Preparation
2.5 AM-Specific Data Preparation
2.6 Feature Subset Selection
2.7 Feature Generation Through Transformation
2.8 Feature Generation Through Learning
2.9 Knowledge-Driven Feature Engineering
2.10 Integrated Feature Engineering
2.11 Feature Operations and Libraries
References
3 Applications in Data-Driven Additive Manufacturing
3.1 Engineering of Design Features
3.2 Feature Engineering at AM Process Phase
3.3 Engineering of Generic Process Features
3.4 Engineering of Process Features: Planning
3.5 Engineering of Process Features: Parametric
3.6 Engineering of Process Features: Layer
3.7 Engineering of Process Features: Melt Pool
3.8 Engineering of Process Features: In-Situ Geometry
3.9 Engineering of Macrostructural Features
3.10 Engineering of Microstructural Features
References
4 Analyzing Additive Manufacturing Feature Spaces
4.1 Design Feature Space
4.2 Process Feature Space
4.3 Post-process Feature Space
References
5 Challenges and Opportunities in Additive Manufacturing Data Preparation
5.1 Challenges
5.2 Opportunities
References
6 Summary
