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Year of publication
Subject
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Additive manufacturing 3 Additive Manufacturing (AM) 2 Computed Tomography (CT) 2 Convolutional neural networks (CNN) 2 EXplainable Artificial Intelligence (XAI) 2 Flaw detection 2 Geometrical deviation compensation 2 Internal defects 2 LPBF 2 Laser Powder Bed Fusion (PBF-LB/M, L-PBF) 2 Laser-Powder Bed Fusion (L-PBF) 2 Machine Learning (ML) 2 Machine learning 2 Non Destructive Testing (NDT) 2 Online monitoring 2 Predeformation 2 Preforming 2 SWIR thermography 2 Selective Laser Melting (SLM) 2 Selective laser melting 2 Series production 2 Additive Fertigung 1 Artificial intelligence 1 Forecasting model 1 GAN 1 Industrie 1 Künstliche Intelligenz 1 L-PBF 1 LSTM 1 Manufacturing industries 1 Prognoseverfahren 1 in-situ monitoring 1 machine learning 1
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Online availability
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Free 6 Undetermined 1
Type of publication
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Article 7
Type of publication (narrower categories)
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Article 6 Article in journal 1 Aufsatz in Zeitschrift 1
Language
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English 7
Author
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Altenburg, Simon J. 2 Bordekar, Harsh 2 Breese, Philipp P. 2 Brysch, Marco 2 Cersullo, Nicola 2 Habiba, Abdelrahman 2 Hartmann, Christoph 2 Hühne, Christian 2 Lechner, Philipp 2 Mohr, Gunther 2 Oster, Simon 2 Philipp, Jens 2 Ulbricht, Alexander 2 Wolf, Daniel 2 Lu, Yan 1 Rai, Rahul 1 Sahu, Chandan Kumar 1 Singh, Shubhendu Kumar 1 Yang, Zhuo 1 Zhang, Zhibo 1
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Published in...
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Journal of Intelligent Manufacturing 6 International journal of production research 1
Source
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EconStor 6 ECONIS (ZBW) 1
Showing 1 - 7 of 7
Cover Image
eXplainable artificial intelligence for automatic defect detection in additively manufactured parts using CT scan analysis
Bordekar, Harsh; Cersullo, Nicola; Brysch, Marco; … - In: Journal of Intelligent Manufacturing (2023), pp. 1-18
Additive Manufacturing (AM) and in particular has gained significant attention due to its capability to produce complex geometries using various materials, resulting in cost and mass reduction per part. However, metal AM parts often contain internal defects inherent to the manufacturing process....
Persistent link: https://www.econbiz.de/10015175562
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Machine learning based prediction of melt pool morphology in a laser-based powder bed fusion additive manufacturing process
Zhang, Zhibo; Sahu, Chandan Kumar; Singh, Shubhendu Kumar; … - In: International journal of production research 62 (2024) 5, pp. 1803-1817
Persistent link: https://www.econbiz.de/10014546305
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A deep learning framework for defect prediction based on thermographic in-situ monitoring in laser powder bed fusion
Oster, Simon; Breese, Philipp P.; Ulbricht, Alexander; … - In: Journal of Intelligent Manufacturing 35 (2023) 4, pp. 1687-1706
The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data,...
Persistent link: https://www.econbiz.de/10015179593
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Deviation compensation in LPBF series production via statistical predeformation and structural pattern analysis
Lechner, Philipp; Hartmann, Christoph; Wolf, Daniel; … - In: Journal of Intelligent Manufacturing 35 (2023) 6, pp. 2645-2652
This article proposes two approaches for a tailored geometrical deviation compensation for Laser-Powder-Bed-Fusion production. The deviation compensation is performed by a non-rigid deformation of the manufacturing geometry in each iteration to reduce the geometrical deviations from the target...
Persistent link: https://www.econbiz.de/10015328824
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Cover Image
Deviation compensation in LPBF series production via statistical predeformation and structural pattern analysis
Lechner, Philipp; Hartmann, Christoph; Wolf, Daniel; … - In: Journal of Intelligent Manufacturing 35 (2023) 6, pp. 2645-2652
This article proposes two approaches for a tailored geometrical deviation compensation for Laser-Powder-Bed-Fusion production. The deviation compensation is performed by a non-rigid deformation of the manufacturing geometry in each iteration to reduce the geometrical deviations from the target...
Persistent link: https://www.econbiz.de/10015402137
Saved in:
Cover Image
A deep learning framework for defect prediction based on thermographic in-situ monitoring in laser powder bed fusion
Oster, Simon; Breese, Philipp P.; Ulbricht, Alexander; … - In: Journal of Intelligent Manufacturing 35 (2023) 4, pp. 1687-1706
The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data,...
Persistent link: https://www.econbiz.de/10015403246
Saved in:
Cover Image
eXplainable artificial intelligence for automatic defect detection in additively manufactured parts using CT scan analysis
Bordekar, Harsh; Cersullo, Nicola; Brysch, Marco; … - In: Journal of Intelligent Manufacturing 36 (2023) 2, pp. 957-974
Additive Manufacturing (AM) and in particular has gained significant attention due to its capability to produce complex geometries using various materials, resulting in cost and mass reduction per part. However, metal AM parts often contain internal defects inherent to the manufacturing process....
Persistent link: https://www.econbiz.de/10015408327
Saved in:
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