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Learning anomaly detection in nanoindentation to identify crystal elastoplasticity properties at the sub-grain level

Thesis proposal

Area of expertiseMechanics
Doctoral SchoolISMME - Systems Engineering, Materials, Mechanics, Energy
SupervisorM. David RYCKELYNCK
Research unitCentre of materials
Starting dateOctober 2nd 2023
KeywordsMachine learning, Anomaly detection, Plasticity, Constitutive equations
AbstractDescription of the PhD project :
The present PhD project will focus on anomaly detection and images-based machine learning to correlate hardness and reduced modulus of oxidised and non-oxidised Ni-based and Ti-based materials as a function of the grain orientation, the local chemical composition and metallurgical state as well as the proximity of grain boundaries. High resolution nanoindentation maps either in a conventional mode or a continuous stiffness measurement mode will be performed to assess the local mechanical properties.

In the present project, the PhD student will:
- Perform nanoindentation tests on model materials to get more experimental data;
- Data augmentation by using a pre-existing crystal plasticity finite element model with strain gradient plasticity simulating the nanoindentation test in a single crystal;
- Develop a machine learning code sampling multimodal data to fastly predict the nanoindentation properties as a function of grain orientation, chemical composition and the metallurgical state;
- To detect surface events/anomalies from surface observation related to the nanoindentation response;
- Cluster data to identify set of microstructural features favouring particular mechanical response (slip activity, hardness-elastic response relationship, etc.);
- Identify parameters of the crystal plasticity finite element model using machine learning;
ProfileEngineer and / or Master of Science - Good level of general and scientific culture. Good level of knowledge of French (B2 level in french is required) and English. (B2 level in english is required) Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Teaching skills. Motivation for research activity. Coherent professional project.

Prerequisite (specific skills for this thesis):
Expected skills and/or know-how :
The PhD student should have the following skills and/or know-how:
- Scientific computing (Python language, etc.);
- Mechanical engineering (and more particularly at the microscale);
- Material sciences and/or computational solid mechanics.

Applicants should supply the following :
- a detailed resume
- a copy of the identity card or passport
- a covering letter explaining the applicant’s motivation for the position
- detailed exam results
- two references : the name and contact details of at least two people who could be contacted
- to provide an appreciation of the candidate
- Your notes of M1, M2
- level of English equivalent TOEIC

to be sent to recrutement_these@mat.mines-paristech.fr
FundingFinancement d'une association ou fondation

- MINES ParisTech


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