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2023 | Buch

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Engineering of Additive Manufacturing Features for Data-Driven Solutions

Sources, Techniques, Pipelines, and Applications

verfasst von: Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao

Verlag: Springer Nature Switzerland

Buchreihe : SpringerBriefs in Applied Sciences and Technology

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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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Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract

This chapter introduces additive manufacturing (AM) in terms of its merits and maturity. The status of data-driven AM research is evaluated in terms of existing reviews. The significance of feature engineering in AM is explained. Finally, the methodology used to collect literature is explained toward the end.

Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao

Chapter 2. Feature Engineering in Additive Manufacturing

Abstract

This chapter provides an overview of feature engineering landscape in additive manufacturing (AM). Domains and paradigms linked to data-driven AM are introduced and discussed. The sources of AM features are introduced in terms of their nature, properties, information variation, and digital representation. A comprehensive introduction to feature engineering techniques for AM is made, which are divided into five broad categories, namely subset selection, generation through transformation, generation through learning, knowledge-driven feature engineering, and integrated feature engineering. As a prerequisite to feature engineering, generic and AM-specific preprocessing techniques are discussed. At the end, different feature operations and libraries relevant to AM are introduced. These techniques are referenced in the next chapter on feature engineering applications.

Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao

Chapter 3. Applications in Data-Driven Additive Manufacturing

Abstract

AM lifecycle is divided into major sources, and preparation of data generated by each source is discussed separately. These sources of features can be grouped into design, process, and post-process categories. In each of these categories, major sources of additive manufacturing (AM) data are identified and used to group the applications of feature engineering in AM. Their applications are discussed in detail in the subsequent sections of this chapter. A key feature of this chapter is its tabular summaries where detailed feature engineering pipelines are presented and linked with feature source, feature form, and feature applications.

Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao

Chapter 4. Analyzing Additive Manufacturing Feature Spaces

Abstract

This chapter analyzes featurization efforts at the design, process, and post-process phases of AM to look for trends and insights related to feature sources, feature engineering techniques and pipelines, feature types, and their applications. The insights are summarized in figures and bar graphs.

Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao

Chapter 5. Challenges and Opportunities in Additive Manufacturing Data Preparation

Abstract

This chapter briefly summarizes the existing challenges and opportunities concerned with AM data preparation in the context of data-driven applications.

Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao

Chapter 6. Summary

Abstract

This chapter summarizes the research brief.

Mutahar Safdar, Guy Lamouche, Padma Polash Paul, Gentry Wood, Yaoyao Fiona Zhao

Titel: Engineering of Additive Manufacturing Features for Data-Driven Solutions
verfasst von: Mutahar Safdar
Guy Lamouche
Padma Polash Paul
Gentry Wood
Yaoyao Fiona Zhao
Copyright-Jahr: 2023
Verlag: Springer Nature Switzerland
Electronic ISBN: 978-3-031-32154-2
Print ISBN: 978-3-031-32153-5
DOI: https://doi.org/10.1007/978-3-031-32154-2

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