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Engineering of additive manufacturing features for data-driven solutions: sources, techniques, pipelines, and applications

Par Conseil national de recherches du Canada

DOITrouver le DOI : https://doi.org/10.1007/978-3-031-32154-2
AuteurRechercher : ; Rechercher : 1; Rechercher : ; Rechercher : ; Rechercher : Identifiant ORCID : https://orcid.org/0000-0003-4927-0514
Affiliation
  1. Conseil national de recherches du Canada. Technologies numériques
FormatTexte, Livre
ISBN978-3-031-32153-5
978-3-031-32154-2
Description physique141 p.
Sujetdata-driven additive manufacturing; feature engineering; data preparation and preprocessing; raw data transformations; representation learning; knowledge engineering; subset selection; mechanistic engineering; 3D printing; machine learning application
Résumé
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.
Date de publication
Maison d’éditionSpringer
Série
  • SpringerBriefs in Applied Sciences and Technology (1 juin 2023).
Langueanglais
Publications évaluées par des pairsOui
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Identificateur de l’enregistrement36fad027-d2aa-4a27-afc4-0feceb5a3064
Enregistrement créé2023-06-06
Enregistrement modifié2023-06-08
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