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Engineering of additive manufacturing features for data-driven solutionssources, techniques, pipelines, and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Engineering of additive manufacturing features for data-driven solutionsby Mutahar Safdar ... [et al.].
其他題名:	sources, techniques, pipelines, and applications /
其他作者:	Safdar, Mutahar.
出版者:	Cham :Springer Nature Switzerland :2023.
面頁冊數:	1 online resource (xv, 141 p.) :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Additive manufacturingData processing.
電子資源:	https://doi.org/10.1007/978-3-031-32154-2
ISBN:	9783031321542$q(electronic bk.)

Engineering of additive manufacturing features for data-driven solutionssources, techniques, pipelines, and applications /
Engineering of additive manufacturing features for data-driven solutionssources, techniques, pipelines, and applications /[electronic resource] :by Mutahar Safdar ... [et al.]. - Cham :Springer Nature Switzerland :2023. - 1 online resource (xv, 141 p.) :ill. (some col.), digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-5318. - SpringerBriefs in applied sciences and technology..

Introduction -- Feature Engineering in AM -- Applications in Data-driven AM -- Analyzing AM Feature Spaces -- Challenges and Opportunities in AM Data Preparation -- 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.

ISBN: 9783031321542$q(electronic bk.)

Standard No.: 10.1007/978-3-031-32154-2doiSubjects--Topical Terms:

944431
Additive manufacturing
--Data processing.

LC Class. No.: TS183.25

Dewey Class. No.: 621.988

Engineering of additive manufacturing features for data-driven solutionssources, techniques, pipelines, and applications /
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245 0 0 $a Engineering of additive manufacturing features for data-driven solutions $h [electronic resource] : $b sources, techniques, pipelines, and applications / $c by Mutahar Safdar ... [et al.].
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505 0 $a Introduction -- Feature Engineering in AM -- Applications in Data-driven AM -- Analyzing AM Feature Spaces -- Challenges and Opportunities in AM Data Preparation -- Summary.
520 $a 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.
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