02/06/2020
The Human Resources Strategy for Researchers

PhD contract in the field of Computer science and mathematics financed during 3 years by the University Clermont Auvergne

This job offer has expired


  • ORGANISATION/COMPANY
    Université Clermont Auvergne
  • RESEARCH FIELD
    Computer science
    Mathematics
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    28/06/2020 00:00 - Europe/Brussels
  • LOCATION
    France › AUBIERE
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    35 H
  • OFFER STARTING DATE
    01/10/2020
  • REFERENCE NUMBER
    UCA/ANR/009
  • IS THE JOB RELATED TO STAFF POSITION WITHIN A RESEARCH INFRASTRUCTURE?
    Yes

Subject:

Development of a convolutional neural network for the real-time measurement of

displacement and strain fields on the surface of deformed structures

Supervisor: Michel GREDIAC

Laboratory: Institut Pascal, UMR CNRS 6605

Email and phone: michel.grediac@uca.fr, 0649232265

Co-advisor(s): François Berry, Benoît Blaysat

Abstract (up to 10 lines):

Digital Image Correlation (DIC) has progressively become the reference technique for

measuring displacement and strain fields on specimens subjected to various

thermomechanical loadings. A range of different industries such as robotics, civil

engineering, automotive industry or aeronautics are concerned. DIC has however some

serious limitations such as calculating time or metrological performance. This PhD work

will mainly consist of developing a convolution neural network dedicated to this type of

measurement, but with better global performance than DIC, as a recent feasibility study

appears to be suggesting. The final goal will be to implement this network on an

embedded system able to provide with high accuracy real-time displacement and strain

maps on specimens subjected to weak deformation.

Skills:

- basic education in signal processing, image processing and/or mechatronics

- applicants showing a strong appetence for computational mechanics or for the

development of full-field measurement techniques will also be considered.

Keywords:

Convolutional Neural Network, Full-field measurements, Metrology, Photomechanics,

Sub-pixel resolution

Description (up to 1 page):

This project deals with the use of images in order to measure displacement and strain fields

on the surface of flat objects subjected to thermomechanical loads. Various fields dealing

with mechanics of materials and structures are concerned such as aeronautics or civil

engineering. Various techniques have been developed to perform such measurements, in

particular Digital Image Correlation [1], have been developed to reach this goal. They

feature however some limitations, especially in terms of processing time and metrological

performance, the displacements and strains to be measured generally featuring a low

amplitude.

A preliminary feasibility study has been recently carried out. It has led to the definition of a

convolutional neural network and a daset suitable for training this network. This dataset

comprises speckle images, which are deformed through well-chosen sub-pixel displacement

fields. This network trained on this dataset features a global performance, which is

equivalent to, if not better than that of DIC [2]. This gives us a glimpse of real-time

displacement and strain field measurement with better metrological performance that

existing tools.

In this context, this work consists of different tasks:

- The first goal will be to better understand the interaction between performance of

the network and definition of the synthetic images of the dataset in terms of nature

of the speckle, displacement fields used to generate the images, and computing

time. The assumptions under which these images are defined and their impact on

the quality of the final results will also be investigated. The speckle rendering system

developed by the supervising team [3] may be used for this purpose.

- The network proposed in [2] was developed on the basis of other networks

dedicated to other applications, in particular concerning displacements, which

amplitude was much greater than one pixel, while the challenge here is to properly

measure displacements with an uncertainty lower than one hundredth of pixel. It

will therefore be necessary to redesign completely the network, under the

constraint to limit as much as possible the number of layers, and thus the number

of coefficients to be determined during the training phase.

- The network will be embedded on a small computer (typicall a NVIDIA Jetson), to

have at the end a compact system providing real-time displacement and strain maps.

- Depending on the work progress, various cameras will be associated in order to get

a stereo system providing tridimensional displacement fields.

Various real experiments will be carried out in order to evidence the benefit of using this

new approach for measuring displacement and strain fields in mechanics of materials and

structures, with substantial industrial applications.

Supervision will be carried out by a pluridisciplinary team composed of experts in embedded

systems, signal processing (F. Berry) and photomechanics (B. Blaysat, M. Grédiac).

Références (up to ½ page):

[1] M. Sutton, J.J. Orteu, and H. Schreier. Image Correlation for Shape, Motion and

Deformation Measurements. Basic Concepts, Theory and Applications. Springer, 2009

[2] S. Boukhtache, K. Abdelouahab, F. Berry, B. Blaysat, F. Sur, M. Grédiac. When Deep

Learning meets Digital Image Correlation. Submitted, 2020.

[3] F. Sur, B. Blaysat, M. Grédiac. Rendering deformed speckle images with a Boolean model.

Journal of Mathematical Imaging and Vision, 2018.

How to candidate?

Contact the supervisor

 
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Benefits

 

 
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Eligibility criteria

 
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Selection process

 

 
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Offer Requirements

  • REQUIRED EDUCATION LEVEL
    Other: Master Degree or equivalent

Skills/Qualifications

 

 
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Specific Requirements

 

 
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Work location(s)
1 position(s) available at
Pascal Institute (IP)
France
Région Auvergne Rhône-Alpes
AUBIERE
63178
Campus Universitaire des Cézeaux TSA 60026 CS 60026 4, Avenue Blaise Pascal

Open, Transparent, Merit based Recruitment procedures of Researchers (OTM-R)

Know more about it at Université Clermont Auvergne

Know more about OTM-R

EURAXESS offer ID: 528432

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