Development and Implementation of a Deep Learning Algorithm to Evaluate the Powder Distribution Process During 3D Printing Using the LPBF Method
This article presents research work on an intelligent system that was developed to monitor and continuously evaluate the quality of metal powder distribution in the laser powder bed fusion (LPBF) 3D printing process. The 3D printer that was used to carry out the work was equipped with an industrial...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/24/11718 |
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| author | Marcin Korzeniowski Aleksandra Maria Małachowska Maciej Szymański |
| author_facet | Marcin Korzeniowski Aleksandra Maria Małachowska Maciej Szymański |
| author_sort | Marcin Korzeniowski |
| collection | DOAJ |
| description | This article presents research work on an intelligent system that was developed to monitor and continuously evaluate the quality of metal powder distribution in the laser powder bed fusion (LPBF) 3D printing process. The 3D printer that was used to carry out the work was equipped with an industrial vision system to capture images immediately after spreading powder on the work field. The powder distribution tests showed that the most common defects were identified as an insufficiently thick layer of powder applied to the working field (super elevation), unevenly distributed powder as a result of recoater vibration (so called recoater hopping), and its wear (so called recoater streaking). In the first stage of research, a set of training data (images) was collected. Then, the implementation of the machine learning process was prepared in the Roboflow environment. After that, the learning, validation, and prediction process was carried out several times using the selected machine learning model (YOLO model implemented in a Python environment) in order to select the most effective parameters. The study showed that deep machine learning can be effectively used to identify defects in powder distribution during the laser powder bed fusion (LPBF) process. |
| format | Article |
| id | doaj-art-c2322c86ad2443f28dd2db7a9777d65f |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c2322c86ad2443f28dd2db7a9777d65f2024-12-27T14:08:09ZengMDPI AGApplied Sciences2076-34172024-12-0114241171810.3390/app142411718Development and Implementation of a Deep Learning Algorithm to Evaluate the Powder Distribution Process During 3D Printing Using the LPBF MethodMarcin Korzeniowski0Aleksandra Maria Małachowska1Maciej Szymański2Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, 50-370 Wrocław, PolandFaculty of Mechanical Engineering, Wroclaw University of Science and Technology, 50-370 Wrocław, PolandFaculty of Mechanical Engineering, Wroclaw University of Science and Technology, 50-370 Wrocław, PolandThis article presents research work on an intelligent system that was developed to monitor and continuously evaluate the quality of metal powder distribution in the laser powder bed fusion (LPBF) 3D printing process. The 3D printer that was used to carry out the work was equipped with an industrial vision system to capture images immediately after spreading powder on the work field. The powder distribution tests showed that the most common defects were identified as an insufficiently thick layer of powder applied to the working field (super elevation), unevenly distributed powder as a result of recoater vibration (so called recoater hopping), and its wear (so called recoater streaking). In the first stage of research, a set of training data (images) was collected. Then, the implementation of the machine learning process was prepared in the Roboflow environment. After that, the learning, validation, and prediction process was carried out several times using the selected machine learning model (YOLO model implemented in a Python environment) in order to select the most effective parameters. The study showed that deep machine learning can be effectively used to identify defects in powder distribution during the laser powder bed fusion (LPBF) process.https://www.mdpi.com/2076-3417/14/24/11718selective laser melting3D printingvision systemsartificial intelligence |
| spellingShingle | Marcin Korzeniowski Aleksandra Maria Małachowska Maciej Szymański Development and Implementation of a Deep Learning Algorithm to Evaluate the Powder Distribution Process During 3D Printing Using the LPBF Method Applied Sciences selective laser melting 3D printing vision systems artificial intelligence |
| title | Development and Implementation of a Deep Learning Algorithm to Evaluate the Powder Distribution Process During 3D Printing Using the LPBF Method |
| title_full | Development and Implementation of a Deep Learning Algorithm to Evaluate the Powder Distribution Process During 3D Printing Using the LPBF Method |
| title_fullStr | Development and Implementation of a Deep Learning Algorithm to Evaluate the Powder Distribution Process During 3D Printing Using the LPBF Method |
| title_full_unstemmed | Development and Implementation of a Deep Learning Algorithm to Evaluate the Powder Distribution Process During 3D Printing Using the LPBF Method |
| title_short | Development and Implementation of a Deep Learning Algorithm to Evaluate the Powder Distribution Process During 3D Printing Using the LPBF Method |
| title_sort | development and implementation of a deep learning algorithm to evaluate the powder distribution process during 3d printing using the lpbf method |
| topic | selective laser melting 3D printing vision systems artificial intelligence |
| url | https://www.mdpi.com/2076-3417/14/24/11718 |
| work_keys_str_mv | AT marcinkorzeniowski developmentandimplementationofadeeplearningalgorithmtoevaluatethepowderdistributionprocessduring3dprintingusingthelpbfmethod AT aleksandramariamałachowska developmentandimplementationofadeeplearningalgorithmtoevaluatethepowderdistributionprocessduring3dprintingusingthelpbfmethod AT maciejszymanski developmentandimplementationofadeeplearningalgorithmtoevaluatethepowderdistributionprocessduring3dprintingusingthelpbfmethod |