[STAGE MASTER] - A Data-Driven Approach to defect detection: Case of Composite Structures

Job description

Keywords: Defect detection, Inverse Problem, Data-Driven approach, sparse identification.

Identifying defects in civil infrastructures is a crucial step in guaranteeing its safety and longevity. Traditional inspections based on visual surveys and destructive testing are often costly, slow and limited in accuracy.

Over the last ten years, research in the field of Numerical Mechanics has focused on integrating recent advances in Data Science with traditional mechanical methods. The intuition behind most of this work is that the exploitation of data is not intended to supplant the resolution of physical models, but rather to create a synergy between data and knowledge models in order to make the most of the contributions of each.

Numerous methods are beginning to emerge from this work, and this internship proposes to explore the possibility of taking advantage of some of them (e.g. [1] - in particular chap. 7 - or [2]) to identify anomalies (non-linearity of the behavior law) in inspected mechanical structures.

Based on synthetic measurements (deformations, etc.), generated using finite element models simulating dynamic or quasi-static behaviors of the structure in the presence of anomalies, the aim will be to solve an inverse problem in order to detect potential defects such as cracks, delaminations or material degradations.

The work will be carried out on a typical civil engineering structure (e.g. a concrete beam or a composite material structure). Note: for the purposes of this internship, measurements will be simulated exclusively using a finite element code. There will be no experimental part in this work.

Objectives

The main goals of this internship is to explore innovative methods for defect detection in mechanical structures by leveraging recent advancements in data-driven approaches. The study will aim to integrate computational mechanics techniques with data science methods to solve inverse problems that arise during the analysis of structural anomalies. They can be summarized as follows :

1- Comprehensive literature review

1. Conduct a survey on recent methods of sparse identification of governing equations from data.
2. Conduct a survey on recent methods of data-driven approaches to solve inverse problems.

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2- Develop a Defect Detection Methodology

1. Generate synthetic measurements using finite element simulations to represent a structure with defect.
2. Implement and test an algorithm to solve the inverse problem of defect identification.
3. Evaluate the performance of the algorithm for different types of defects.

Expected scientific/technical production

The main outcome expected by the end of the internship is :

• Conference Paper: Presenting the proposed method and results to an international conference.

Introduction to the laboratory CESI LINEACT- Research Unit

CESI LINEACT (Digital Innovation Laboratory for Companies and Learnings at the service of the territories competitiveness) is the CESI group laboratory whose activities are implemented on CESI campuses.

Link to the laboratory website:

https://lineact.cesi.fr/en/

https://lineact.cesi.fr/en/research-unit/presentation-lineact/

CESI LINEACT (EA 7527), Digital Innovation Laboratory for Business and Learning at the service of the Competitiveness of Territories, anticipates and accompanies the technological mutations of the sectors and services related to industry and construction. CESI's historical proximity to companies is a determining factor for our research activities and has led us to focus our efforts on applied research close to companies and in partnership with them. A human-centered approach coupled with the use of technologies, as well as the territorial network and the links with training, have allowed us to build transversal research; it puts the human being, his needs and his uses, at the center of its problems and approaches the technological angle through these contributions.

Its research is organized according to two interdisciplinary scientific themes and two application areas.

• Theme 1 "Learning and Innovation" is mainly concerned with Cognitive Sciences, Social Sciences and Management Sciences, Training Sciences and Techniques and Innovation Sciences. The main scientific objectives of this theme are to understand the effects of the environment, and more particularly of situations instrumented by technical objects (platforms, prototyping workshops, immersive systems, etc.) on the learning, creativity and innovation processes.
• Theme 2 "Engineering and Digital Tools" is mainly concerned with Digital Sciences and Engineering. The main scientific objectives of this theme concern the modeling, simulation, optimization and data analysis of industrial or urban systems. The research work also focuses on the associated decision support tools and on the study of digital twins coupled with virtual or augmented environments.

These two teams develop and cross their research in application areas such as

• Industry 5.0,
• Construction 4.0 and Sustainable City,
• Digital Services.

Areas supported by research platforms, mainly that in Rouen dedicated to Factory 5.0 and those in Nanterre dedicated to Factory 5.0 and Construction 4.0.

Links to the research axes of the research team involved

CESI Lineact Research Thematic: Modeling, Desing, and Architecture of CPS.

Desired profile

Your application must include :

• A detailed curriculum vitae.
• A cover letter explaining why the candidate is interested in this internship.
• Master 1 and 2 transcripts (to be adapted to the level of the internship)
• Recommendation letters if available
• Any other documents you consider useful such as project reports, publications, datasets, codes, related to this internship topic.

Please send all documents in one file.

The Candidate’s Profile

The candidate should be a Master’s student (M2) or in the final year of an Engineering School program, with a background in Computational Mechanics, Applied Mathematics, or Data Science, and an interest in all three fields.

She/He should have some knowledge and experience in a number of the following topics:

• Finite Element Modeling and Computational Mechanics.
• Programming experience with interpreted languages such as MATLAB, Python, or similar.
• Fluent written and verbal communication skills in English are required

Bibliography:

[1] Brunton, S. L., & Kutz, J. N. (2019). Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press.

[2] Flaschel, M., Kumar, S., & De Lorenzis, L. (2021). Unsupervised discovery of interpretable hyperelastic constitutive laws. Computer Methods in Applied Mechanics and Engineering, 381, 113852.

[3] Ladevèze, P., & Chamoin, L. (2016). The constitutive relation error method: A general verification tool. Verifying Calculations-Forty Years On, 59-94.

[4] Seidl, D. T., Oberai, A. A., & Barbone, P. E. (2019). The Coupled Adjoint-State Equation in forward and inverse linear elasticity: Incompressible plane stress. Computer Methods in Applied Mechanics and Engineering, 357, 112588.

[5] Ferrier, R., Cocchi, A., & Hochard, C. (2021). Modified Constitutive Relation Error for field identification: theoretical and experimental assessments on fiber orientation identification in a composite material. International Journal for Numerical Methods in Engineering.