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Record Nr. |
UNINA9910728930303321 |
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Autore |
Safdar Mutahar |
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Titolo |
Engineering of Additive Manufacturing Features for Data-Driven Solutions : Sources, Techniques, Pipelines, and Applications / / by Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (151 pages) |
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Collana |
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SpringerBriefs in Applied Sciences and Technology, , 2191-5318 |
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Altri autori (Persone) |
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LamoucheGuy |
PaulPadma Polash |
WoodGentry |
ZhaoYaoyao Fiona |
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Disciplina |
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Soggetti |
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Industrial engineering |
Production engineering |
Engineering - Data processing |
Artificial intelligence |
Machine learning |
Education |
Industrial and Production Engineering |
Data Engineering |
Artificial Intelligence |
Machine Learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- Feature Engineering in AM -- Applications in Data-driven AM -- Analyzing AM Feature Spaces -- Challenges and Opportunities in AM Data Preparation -- Summary. |
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Sommario/riassunto |
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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 |
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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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