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Estimating manufacturing parameters of additively manufactured 316L steel cubes using ultrasound fingerprinting
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. (Signal Processing)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
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.ORCID iD: 0000-0003-2560-5703
2023 (English)In: Research and Review Journal of Nondestructive Testing (ReJNDT), ISSN 2941-4989, Vol. 1, no 1, article id 28214Article in journal (Refereed) Published
Abstract [en]

Metal based additive manufacturing techniques such as laser powder bed fusion (LPBF) can produce parts with complex designs as compared to traditional manufacturing. The quality is affected by defects such as porosity or lack of fusion that can be reduced by online control of manufacturing parameters. The conventional way of testing is time consuming and does not allow the process parameters to be linked to the mechanical properties. In this paper, ultrasound data along with supervised learning is used to estimate the manufacturing parameters of 316L steel cubes. Nine cubes with varying manufacturing parameters (speed, hatch distance and power) are examined with ultrasound using focused transducers. The volumetric energy density (VED) is calculated from the process parameters for each cube. The ultrasound scans are performed in a dense grid in the built and transverse direction. The ultrasound data is used in partial least square regression algorithm by labelling the data with speed, hatch distance and power and then by labelling the same data with the VED. These models are computed for both measurement directions and as the samples are anisotropic, we see different behaviours of estimation in each direction. The model is then validated with an unknown set from the same 9 cubes. The manufacturing parameters are estimated and validated with a good accuracy making way for online process control.

Place, publisher, year, edition, pages
NDT.net , 2023. Vol. 1, no 1, article id 28214
Keywords [en]
3D-printing, supervised learning, signal processing, ultrasound fingerprinting
National Category
Signal Processing Metallurgy and Metallic Materials Manufacturing, Surface and Joining Technology
Research subject
Signal Processing; Engineering Materials
Identifiers
URN: urn:nbn:se:ltu:diva-99218DOI: 10.58286/28214OAI: oai:DiVA.org:ltu-99218DiVA, id: diva2:1782960
Conference
13th European Conference on Non-Destructive Testing (ECNDT23), Lisbon, Portugal, July 3-7, 2023
Note

Godkänd;2023;Nivå 0;2023-08-10 (hanlid);Konferensartikel i tidskrift

Available from: 2023-07-18 Created: 2023-07-18 Last updated: 2024-01-18Bibliographically 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, PiaMishra, Pragya

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