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Giulio Masinelli
Researcher at Swiss Federal Laboratories for Materials Science and Technology
Publications - 16
Citations - 334
Giulio Masinelli is an academic researcher from Swiss Federal Laboratories for Materials Science and Technology. The author has contributed to research in topics: Computer science & Process (computing). The author has an hindex of 3, co-authored 7 publications receiving 91 citations.
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Journal ArticleDOI
Deep Learning for In Situ and Real-Time Quality Monitoring in Additive Manufacturing Using Acoustic Emission
TL;DR: This paper is a supplement to existing studies in this field and proposes a unique combination of highly sensitive acoustic sensor and machine learning for process monitoring of AM processes since it requires minimum modifications of commercially available industrial machines.
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Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance
Sergey Shevchik,Tri Le-Quang,B. Meylan,Farzad Vakili Farahani,Margie P. Olbinado,Alexander Rack,Giulio Masinelli,Christian Leinenbach,Kilian Wasmer +8 more
TL;DR: A method for real-time detection of process instabilities that can lead to defects that can be exploited to provide feedbacks in a closed-loop quality control system is proposed.
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Semi-supervised Monitoring of Laser powder bed fusion process based on acoustic emissions
Vigneashwara Pandiyan,Rita Drissi-Daoudi,Sergey Shevchik,Giulio Masinelli,Tri Le-Quang,Roland E. Logé,Kilian Wasmer +6 more
TL;DR: In this paper, the authors propose that metal-based laser powder bed fusion (LPBF) suffers from a lack of repeatability and is challenging to model, making their quality monitoring essential and demanding.
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Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning
R. Drissi-Daoudi,Vigneashwara Pandiyan,Roland E. Logé,Sergey Shevchik,Giulio Masinelli,Hossein Ghasemi-Tabasi,Annapaola Parrilli,Kilian Wasmer +7 more
TL;DR: In this article , the use of a low-cost microphone combined with state-of-the-art machine learning (ML) algorithms as online process monitoring to differentiate various materials and process regimes of Laser-Powder Bed Fusion (LPBF) was investigated.
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Deep Transfer Learning of Additive Manufacturing Mechanisms Across Materials in Metal-Based Laser Powder Bed Fusion Process
Vigneashwara Pandiyan,R. Drissi-Daoudi,Sergey Shevchik,Giulio Masinelli,Tri Le-Quang,Roland E. Logé,Kilian Wasmer +6 more
TL;DR: In this article , the authors demonstrate the knowledge learned by the two native deep learning (DL) networks, namely VGG and ResNets, on four LPBF process mechanisms such as balling, Lack of Fusion (LoF) pores, conduction mode, and keyhole pores in stainless steel (316L) can be transferred to bronze (CuSn8).