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Ultrasonic Assessment of the Effect of Manufacturing Parameters on the Variability Within Additively Manufactured 316L Samples
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-9859-8586
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-6216-6132
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.ORCID iD: 0000-0001-5921-1935
2023 (English)In: 2023 IEEE International Ultrasonics Symposium (IUS), IEEE, 2023Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2023.
Series
IEEE Symposium (IUS) Ultrasonics
National Category
Manufacturing, Surface and Joining Technology Production Engineering, Human Work Science and Ergonomics
Research subject
Signal Processing; Engineering Materials
Identifiers
URN: urn:nbn:se:ltu:diva-102001DOI: 10.1109/IUS51837.2023.10307294Scopus ID: 2-s2.0-85178637902OAI: oai:DiVA.org:ltu-102001DiVA, id: diva2:1808838
Conference
IEEE International Ultrasonics Symposium (IUS 2023), Montreal, Quebec, Canada, September 3-8, 2023
Note

ISBN for host publication: 979-8-3503-4646-6, 979-8-3503-4645-9

Available from: 2023-11-01 Created: 2023-11-01 Last updated: 2024-03-07Bibliographically approved
In thesis
1. Non-destructive assessment of additively manufactured objects using ultrasound
Open this publication in new window or tab >>Non-destructive assessment of additively manufactured objects using ultrasound
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Additive manufacturing (AM) enables the manufacturing of complex and tailored products for an unlimited number of applications such as aerospace, healthcare, etc. The technology has received a lot of attention in lightweight applications where it is associated with new design possibilities but also reduced material costs, material waste, and energy consumption. The use of ultrasound has the potential to become the material characterization method used for AM since it is quick, safe, and scales well with component size. Ultrasound data, coupled with supervised learning techniques, serves as a powerful tool for the non-destructive evaluation of different materials, such as metals.

This research focuses on understanding the additive manufacturing process, the resulting material properties, and the variation captured using ultrasound due to the manufacturing parameters. The case study included in this thesis is the examination of 316L steel cubes manufactured using laser powder bed fusion. This study includes the estimation and prediction of manufacturing parameters using supervised learning, the assessment of the influence of the manufacturing parameters on the variability within samples, and the quantitative quality assessment of the samples based on the material properties that are a result of the changes in manufacturing parameters.

The research is vital for analyzing the homogeneity of microstructures, advancement in online process control, and ensuring the quality of additively manufactured products. This study contributes to valuable insights into the relationship between manufacturing parameters, material properties, and ultrasound signatures. There is a significant variation captured using ultrasound within the samples and between samples that shows the backscattered signal is sensitive to the microstructure that is a result of the manufacturing parameters. Since the material properties change with the change in manufacturing parameters, the quality of a sample can be described by the relation between the material properties and backscattered ultrasound signals.

The thesis is divided into two parts. The first part focuses on the introduction of the study, a summary of the contributions, and future work. The second part contains a collection of papers describing the research in detail.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2024
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Signal Processing
Research subject
Signal Processing
Identifiers
urn:nbn:se:ltu:diva-103796 (URN)978-91-8048-468-8 (ISBN)978-91-8048-469-5 (ISBN)
Presentation
2024-02-29, E632, Luleå University of Technology, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2024-01-18 Created: 2024-01-17 Last updated: 2024-02-08Bibliographically approved

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Zia, ShafaqCarlson, Johan E.Åkerfeldt, Pia

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