Christian Leinenbach
Christian Leinenbach
Empa-Swiss Federal Laboratories for Materials Science and Technology
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Cited by
Cited by
Iron-based shape memory alloys for civil engineering structures: An overview
A Cladera, B Weber, C Leinenbach, C Czaderski, M Shahverdi, ...
Construction and building materials 63, 281-293, 2014
Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks
SA Shevchik, C Kenel, C Leinenbach, K Wasmer
Additive Manufacturing 21, 598-604, 2018
Microstructure and mechanical properties of Al-Mg-Zr alloys processed by selective laser melting
JR Croteau, S Griffiths, MD Rossell, C Leinenbach, C Kenel, V Jansen, ...
Acta Materialia 153, 35-44, 2018
Feasibility of iron-based shape memory alloy strips for prestressed strengthening of concrete structures
C Czaderski, M Shahverdi, R Brönnimann, C Leinenbach, M Motavalli
Construction and Building Materials 56, 94-105, 2014
A novel Fe‐Mn‐Si shape memory alloy with improved shape recovery properties by VC precipitation
Z Dong, UE Klotz, C Leinenbach, A Bergamini, C Czaderski, M Motavalli
Advanced engineering materials 11 (1‐2), 40-44, 2009
Effect of laser rescanning on the grain microstructure of a selective laser melted Al-Mg-Zr alloy
S Griffiths, MD Rossell, J Croteau, NQ Vo, DC Dunand, C Leinenbach
Materials Characterization 143, 34-42, 2018
3D laser shock peening–a new method for the 3D control of residual stresses in selective laser melting
N Kalentics, E Boillat, P Peyre, C Gorny, C Kenel, C Leinenbach, ...
Materials & Design 130, 350-356, 2017
Deep Learning for In Situ and Real-Time Quality Monitoring in Additive Manufacturing Using Acoustic Emission
SA Shevchik, G Masinelli, C Kenel, C Leinenbach, K Wasmer
IEEE Transactions on Industrial Informatics 15 (9), 5194-5203, 2019
Fatigue and cyclic deformation behaviour of surface-modified titanium alloys in simulated physiological media
C Leinenbach, D Eifler
Biomaterials 27 (8), 1200-1208, 2006
Phase transformation behavior under uniaxial deformation of an Fe–Mn–Si–Cr–Ni–VC shape memory alloy
WJ Lee, B Weber, G Feltrin, C Czaderski, M Motavalli, C Leinenbach
Materials Science and Engineering: A 581, 1-7, 2013
Fatigue behavior of a Fe-Mn-Si shape memory alloy used for prestressed strengthening
E Ghafoori, E Hosseini, C Leinenbach, J Michels, M Motavalli
Materials & Design 133, 349-362, 2017
Combining alloy and process modification for micro-crack mitigation in an additively manufactured Ni-base superalloy
S Griffiths, HG Tabasi, T Ivas, X Maeder, A De Luca, K Zweiacker, ...
Additive Manufacturing 36, 101443, 2020
Stress recovery behaviour of an Fe–Mn–Si–Cr–Ni–VC shape memory alloy used for prestressing
WJ Lee, B Weber, G Feltrin, C Czaderski, M Motavalli, C Leinenbach
Smart Materials and Structures 22 (12), 125037, 2013
Microstructure, residual stresses and shear strength of diamond–steel-joints brazed with a Cu–Sn-based active filler alloy
S Buhl, C Leinenbach, R Spolenak, K Wegener
International Journal of Refractory Metals and Hard Materials 30 (1), 16-24, 2012
In situ investigation of phase transformations in Ti-6Al-4V under additive manufacturing conditions combining laser melting and high-speed micro-X-ray diffraction
C Kenel, D Grolimund, X Li, E Panepucci, VA Samson, DF Sanchez, ...
Scientific reports 7 (1), 16358, 2017
Thermo‐mechanical properties of an Fe–Mn–Si–Cr–Ni–VC shape memory alloy with low transformation temperature
C Leinenbach, H Kramer, C Bernhard, D Eifler
Advanced Engineering Materials 14 (1‐2), 62-67, 2012
Impact of Ni content on the thermoelectric properties of half-Heusler TiNiSn
Y Tang, X Li, LHJ Martin, EC Reyes, T Ivas, C Leinenbach, S Anand, ...
Energy & Environmental Science 11 (2), 311-320, 2018
Recovery stress formation in a restrained Fe–Mn–Si-based shape memory alloy used for prestressing or mechanical joining
WJ Lee, B Weber, C Leinenbach
Construction and Building Materials 95, 600-610, 2015
3D laser shock peening–A new method for improving fatigue properties of selective laser melted parts
N Kalentics, MOV de Seijas, S Griffiths, C Leinenbach, RE Loge
Additive Manufacturing 33, 101112, 2020
Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance
S Shevchik, T Le-Quang, B Meylan, FV Farahani, MP Olbinado, A Rack, ...
Scientific reports 10 (1), 3389, 2020
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