Alain Schorderet

Prof. Alain Schorderet

orcid

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

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

Representative Publications

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7 October 2019 in Sensors
Verified
Journal Article
Open access
PDF file
FMFrédéric Meyer
Frédéric Meyer
APAlain Prenleloup
Alain Prenleloup
ASAlain Schorderet
Alain Schorderet
In alpine skiing, understanding the interaction between skiers and snow is of primary importance for both injury prevention as well as performance analysis. Risk of injuries is directly linked to constraints undergone by the skier. A force platform placed as an interface between the ski and the skier should allow a better understanding of these constraints to be obtained to thereby develop a more reliable release system of binding. It should also provide useful information to allow for better physical condition training of athletes and non-professional skiers to reduce the risk of injury. Force and torque measurements also allow for a better understanding of the skiers’ technique (i.e., load evolution during turns, force distribution between left and right leg…). Therefore, the aim of this project was to develop a new embedded force platform that could be placed between the ski boot and the binding. First, the physical specifications of the dynamometer are listed as well as the measurement scope. Then, several iterations were performed on parametric 3D modeling and finite element analysis to obtain an optimal design. Two platforms were then machined and equipped with strain gauges. Finally, the calibration was performed on a dedicated test bench. The accuracy of the system was between 1.3 and 12.8% of the applied load. These results show a very good linearity of the system, which indicate a great outcome of the design. Field tests also highlighted the ease of use and reliability. This new dynamometer will allow skiers to wear their own equipment while measuring force and torque in real skiing conditions.

1 January 2013 in Procedia CIRP
Verified
Journal Article
Open access
ASAlain Schorderet
Alain Schorderet
EDE. Deghilage
E. Deghilage
KAK. Agbeviade
K. Agbeviade
Brittle materials such as ceramics, glasses and oxide single crystals find increasing applications in advanced micro-engineering products. Machining small features in such materials represents a manufacturing challenge. Ultrasonic drilling constitutes a promising technique for realizing simple micro-holes of high diameter-to-depth ratio. The process involves impacting abrasive particles in suspension in a liquid slurry between tool and work piece. Among the process performance criteria, the drilling time (productivity) is one ofthe most important quantities to evaluate the suitability of theprocess for industrial applications. This paper summarizes recent results pertaining to the ultrasonic micro-drilling process obtained with a semi-industrial 3-axis machine. The work piece is vibrated at 40kHz frequency with an amplitude of several micrometers. A voice-coil actuator and a control loop based on the drilling force impose the tool feed. In addition, the tool is rotated at a prescribed speed to improve the drilling speed as well as the hole geometry. Typically, a WC wire serves as tool to bore 100 m and 200 m diameter micro-holes of 300 to 1,000 m depth in glass and ruby. The abrasive slurry contains B4C particles of 1 m to 5 m diameter in various concentrations.This paper discusses, on the basis of the experimental results for glass deep hole micro-machining, the influence of the hole diameter (100 or 200 m), and the type of tool (wire or drill). The use of drills help to keep a higher mean drilling speed while the cylindrical wire tools provide a higher speed (twice) in the first 20% ofthe drilling depth. This study shows that the drilling speed in glass deep icro-drilling is depending on depth and type of tool used

17 December 2020 in Proceedings
Verified
Conference Paper
Open access
PDF file
SCStefano Carrino
Stefano Carrino
JGJonathan Guerne
Jonathan Guerne
JDJonathan Dreyer
Jonathan Dreyer
HGHatem Ghorbel
Hatem Ghorbel
ASAlain Schorderet
Alain Schorderet
RMRaphael Montavon
Raphael Montavon
The online automatic estimation of the quality of products manufactured in any machining process without any manual intervention represents an important step toward a more efficient, smarter manufacturing industry. Machine learning and Convolutional Neural Networks (CNN), in particular, were used in this study for the monitoring and prediction of the machining quality conditions in a high-speed milling of stainless steel (AISI 303) using a 3 mm tungsten carbide. The quality was predicted using the Acoustic Emission (AE) signals captured during the cutting operations. The spectrograms created from the AE signals were provided to the CNN for a 3-class quality level. A promising average f1-score of 94% was achieved.

13 November 2019 in MM Science Journal
Verified
Journal Article
Closed access
ASAlain Schorderet
Alain Schorderet
RHR. Herzog
R. Herzog
NJN. Jacquod
N. Jacquod
YMY. Marchand
Y. Marchand
CPCh. Prongue
Ch. Prongue
Milling parts for watch, medical, aircraft or molds industries is a compromise between time and precision or surface quality. The latter is very often related to machine vibrations during the process. This paper summarizes results obtained with two approaches aiming at reducing machine vibrations...