Application of Data Science and Engineering

William Halsey

doi:10.1016/B978-0-12-819726-4.00118-6

Application of Data Science and Engineering

William Halsey

Manufacturing Systems Analytics

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Metal additive manufacturing (AM) processes exhibit significant variability in the quality and properties of components that are produced. This variability has prevented the widespread adoption of AM in industry. The need for more advanced and descriptive process monitoring, part qualification, and process control has led to an increasing number of sensors on machines and subsequent data to analyze. Increasingly, data science principles are being leveraged in each of these domains in order to process this data and better understand the causes of variability and the corresponding quality inconsistencies that occur in additive manufacturing.

Original language	English
Title of host publication	Encyclopedia of Materials
Subtitle of host publication	Metals and Alloys
Publisher	Elsevier
Pages	212-222
Number of pages	11
ISBN (Electronic)	9780128197264
ISBN (Print)	9780128197332
DOIs	https://doi.org/10.1016/B978-0-12-819726-4.00118-6
State	Published - Sep 1 2021

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10.1016/B978-0-12-819726-4.00118-6

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Application of Data Science and Engineering

Abstract

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