Kilian Wasmer

Dr. Kilian Wasmer

Empa–Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Advanced Materials Processing, Feuerwerkerstrasse 39, CH–3602 Thun, Switzerland
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Research Focus & Metrics

Academic Career Length

25Years (2002-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 May 2018 in Additive Manufacturing
Verified
Journal Article
Closed access
SSS.A. Shevchik
S.A. Shevchik
CKC. Kenel
C. Kenel
CLChristian Leinenbach
Christian Leinenbach
Kilian WasmerKilian Wasmer
Kilian Wasmer

11 April 2019 in IEEE Transactions on Industrial Informatics
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Journal Article
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SSSergey A. Shevchik
Sergey A. Shevchik
GMGiulio Masinelli
Giulio Masinelli
CKChristoph Kenel
Christoph Kenel
CLChristian Leinenbach
Christian Leinenbach
Kilian WasmerKilian Wasmer
Kilian Wasmer
Additive manufacturing (AM) is considered as a revolution in manufacturing. However, the high expectations face technical difficulties that prevent further penetration into wider industries. The main reason is the lack of process reproducibility and the absence of a reliable and cost-effective process monitoring. 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. The acoustic signals from a real powder-bed fusion AM process were collected using a fiber Bragg grating. The process parameters are intentionally tuned to achieve three levels of quality categories, which are related to the porosity contents inside the workpiece. The quality categories are defined as high, medium, and poor quality and their corresponding porosity contents are 0.07%, 0.30%, and 1.42%, respectively. Wavelet spectrograms of the signals and their encoded label representations, obtained from spectral clustering, are taken as features. A deep convolutional neural network is used to classify the features from each category and the classification accuracy ranges between 78% and 91%. Hence, the proposed method has significant industrial potentials for in situ and real-time quality monitoring of AM processes since it requires minimum modifications of commercially available industrial machines.

17 October 2018 in Journal of Materials Engineering and Performance
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Journal Article
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Kilian WasmerKilian Wasmer
Kilian Wasmer
TLT. Le-Quang
T. Le-Quang
BMB. Meylan
B. Meylan
SSS. A. Shevchik
S. A. Shevchik

22 January 2007 in Applied Physics Letters
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Journal Article
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JMJohann Michler
Johann Michler
Kilian WasmerKilian Wasmer
Kilian Wasmer
SMStephan Meier
Stephan Meier
FÖFredrik Östlund
Fredrik Östlund
Klaus LeiferKlaus Leifer
Klaus Leifer
The authors have experimentally investigated the compressive strength of GaAs pillars with a diameter of 1μm by uniaxial compression tests. The tests were performed at room temperature and, contrary to macroscopic tests, the micropillars were found to exhibit ductile plasticity comparable to that of metal single crystal micropillars. The yield stress was 1.8±0.4GPa and, for one pillar that was more closely examined, a total deformation of 24% was observed. In the diffraction patterns from transmission electron microscopy studies of this pillar, a high density of twins was observed.

1 April 2007 in Journal of Materials Research
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Journal Article
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BMB. Moser
B. Moser
Kilian WasmerKilian Wasmer
Kilian Wasmer
LBL. Barbieri
L. Barbieri
JMJ. Michler
J. Michler

25 February 2022 in Virtual and Physical Prototyping
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Journal Article
Open access
RDRita Drissi-Daoudi
Rita Drissi-Daoudi
Vigneashwara PandiyanVigneashwara Pandiyan
Vigneashwara Pandiyan
Roland LogéRoland Logé
Roland Logé
SSSergey Shevchik
Sergey Shevchik
GMGiulio Masinelli
Giulio Masinelli
Kilian WasmerKilian Wasmer
Kilian Wasmer
This study investigates 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). Three processing regimes (lack of fusion pores, conduction mode and keyhole pores) and three alloys (316L stainless steel, bronze (CuSn8), and Inconel 718) were selected. Three conventional ML algorithms and a Convolutional Neural Network (CNN) were chosen to perform the classification tasks resulting in five main findings. First, we proved that the AE features are related to the laser-material interaction and not from undesired machine or environmental noise. Second, the process regimes are classified with high accuracy (> 87%) regardless of the algorithms and materials. Third, it is possible to build a single model from the three materials and still reach high classification accuracy (>86%) of the different regimes. Forth, the AE features used for the classifications are material and regime dependent. Finally, with LPBF processing of multi-materials on the rise, a strategy for classifying the material and the process regimes simultaneously using a CNN multi-label architecture reached a very high classification accuracy (≈ 93%). The results demonstrate the potential of our approaches for online LPBF process monitoring of different materials and regimes.