Engineering of Additive Manufacturing Features for Data-Driven Solutions - Sources, Techniques, Pipelines, and Applications

• Published in: SpringerBriefs in Applied Sciences and Technology
• Main Authors: Safdar, Mutahar, Lamouche, Guy, Paul, Padma Polash, Wood, Gentry, Zhao, Yaoyao Fiona
• Format: eBook Book
• Language: English
• Published: Cham Springer Nature 2023; Springer; Springer Nature Switzerland
• Edition: 1
• Subjects: 3D printing; Additive manufacturing; Artificial Intelligence; Data Engineering; data preparation and preprocessing; data-driven additive manufacturing; Education, general; Engineering; feature engineering; Industrial and Production Engineering; knowledge engineering; Machine Learning; machine learning application; Manufacturing Engineering; Materials & Manufacturing Processes; mechanistic engineering; raw data transformations; representation learning; subset selection
• ISBN: 3031321537; 9783031321535; 3031321545; 9783031321542
• ISSN: 2191-5318; 2191-530X; 2191-5318
• DOI: 10.1007/978-3-031-32154-2

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 will 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.