Title
Engineering of Additive Manufacturing Features for Data-Driven Solutions [electronic resource] : Sources, Techniques, Pipelines, and Applications / by Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao.
Publication
Cham : Springer Nature Switzerland : Imprint: Springer, 2023.
Physical Description
1 online resource (XV, 141 p.) 43 illus., 37 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Added to Catalog
June 09, 2023
Series
SpringerBriefs in Applied Sciences and Technology,
Contents
Introduction
Feature Engineering in AM
Applications in Data-driven AM
Analyzing AM Feature Spaces
Challenges and Opportunities in AM Data Preparation
Summary.
Also listed under
SpringerLink (Online service)