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Process quality assessment with imaging and acoustic monitoring during Laser Powder Bed Fusion
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Chemical Sciences (from 2013).
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Physics (from 2013).
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Physics (from 2013).ORCID iD: 0000-0001-6851-6319
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Physics (from 2013).ORCID iD: 0000-0002-9441-2502
2022 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 111, p. 363-367Article in journal (Refereed) Published
Abstract [en]

Acoustic monitoring of laser powder bed fusion (LPBF) has shown a high sensitivity to stochastic defects, e.g., cracks, pores and lack of fusion (LOF), and melting instability. The advantage of this method is the possibility to filter raw data and extract acoustic signal features for the data analysis, thus minimizing data and computing time. In this research during the build of components from hot work tool steel powder, acoustic signals and powder bed images were acquired for post-process data analysis and search for correlations with LOF. Different densities caused by LOF were obtained by changing the shielding gas velocity. In the analysis, selected combinations of features with the relationship between the build phases and the final properties such as density and surface roughness, were investigated. For the current dataset prediction of the optimal state showed an accuracy of 98%. This investigation suggests the applicability of the smart data-centric machine learning method to predict the relationship of process parameters, monitoring signals, and material properties. © 2022 The Authors. Published by Elsevier B.V.

Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 111, p. 363-367
Keywords [en]
acoustic features, data-centric, laser powder bed fusion, machine learning, material properties, monitoring, smart data, spattering, tool steel, Acoustic waves, Data acquisition, Data handling, Deep learning, Information analysis, Signal processing, Stochastic systems, Surface roughness, Acoustic monitoring, Acoustic signals, Data centric, Laser powders, Machine-learning, Powder bed, SMART datum
National Category
Materials Engineering
Research subject
Mechanical Engineering; Materials Engineering
Identifiers
URN: urn:nbn:se:kau:diva-92582DOI: 10.1016/j.procir.2022.08.167Scopus ID: 2-s2.0-85141895463OAI: oai:DiVA.org:kau-92582DiVA, id: diva2:1714738
Conference
12th CIRP Conference on Photonic Technologies, LANE 2022, 4 September 2022 through 8 September 2022
Available from: 2022-11-30 Created: 2022-11-30 Last updated: 2022-12-07Bibliographically approved

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Zhirnov, IvanPanahi, NegarÅsberg, MikaelKrakhmalev, Pavel

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CiteExportLink to record
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