Unlimited

Authorized users

Can lend chapters, not whole ebooks

Engineering of additive manufacturing features for data-driven solutions : sources, techniques, pipelines, and applications / Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao.

9783031321542 (electronic bk.)
3031321545 (electronic bk.)
9783031321535
3031321537

Cham : Springer, 2023.

English

1 online resource (xv, 141 pages) : illustrations (some color).

10.1007/978-3-031-32154-2 doi

TS183.25

621.9/88

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.

Includes bibliographical references.

Access limited to authorized users.

Online resource; title from PDF title page (SpringerLink, viewed June 8, 2023).

SpringerBriefs in applied sciences and technology, 2191-5318

Print version: 9783031321535

Online Resources > Ebooks
All Resources