Sökning: ((WFRF:(Doring A)) pers:(Metspalu A))
> (2021) >
Process quality ass...
Process quality assessment with imaging and acoustic monitoring during Laser Powder Bed Fusion
-
- Zhirnov, Ivan (författare)
- Karlstads universitet,Institutionen för ingenjörs- och kemivetenskaper (from 2013)
-
- Panahi, Negar (författare)
- Karlstads universitet,Institutionen för ingenjörsvetenskap och fysik (from 2013)
-
- Åsberg, Mikael (författare)
- Karlstads universitet,Institutionen för ingenjörsvetenskap och fysik (from 2013)
-
visa fler...
-
- Krakhmalev, Pavel, Professor, 1973- (författare)
- Karlstads universitet,Institutionen för ingenjörsvetenskap och fysik (from 2013)
-
visa färre...
-
(creator_code:org_t)
- Elsevier, 2022
- 2022
- Engelska.
-
Ingår i: Procedia CIRP. - : Elsevier. - 2212-8271. ; 111, s. 363-367
- Relaterad länk:
-
https://doi.org/10.1...
-
visa fler...
-
https://kau.diva-por... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Materialteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Materials Engineering (hsv//eng)
Nyckelord
- 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
- Maskinteknik
- Mechanical Engineering
- Materials Engineering
- Materialteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas