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

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 an...

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Αποθηκεύτηκε σε:
Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Safdar, Mutahar (Συγγραφέας), Lamouche, Guy (Συγγραφέας), Paul, Padma Polash (Συγγραφέας), Wood, Gentry (Συγγραφέας), Zhao, Yaoyao Fiona (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:Αγγλικά
Έκδοση: Cham : Springer Nature Switzerland : Imprint: Springer, 2023.
Έκδοση:1st ed. 2023.
Σειρά:SpringerBriefs in Applied Sciences and Technology,
Θέματα:
Industrial engineering.
Production engineering.
Engineering > Data processing.
Artificial intelligence.
Machine learning.
Education.
Industrial and Production Engineering.
Data Engineering.
Artificial Intelligence.
Machine Learning.
Διαθέσιμο Online:https://doi.org/10.1007/978-3-031-32154-2
Ετικέτες: Προσθήκη ετικέτας
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MARC

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250 |a 1st ed. 2023. 
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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. 
532 8 |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. 
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532 8 |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com 
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650 0 |a Production engineering. 
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650 0 |a Artificial intelligence. 
650 0 |a Machine learning. 
650 0 |a Education. 
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