Master student internship in Damage prediction in long fiber-reinforced composites using machine-learning algorithms trained by micromechanical data (M/F)

Référence : MRT-2020-Intern-018

Type: Stagiaire
Type de contrat: Stage
Durée: 6 months
Lieu de travail: Hautcharage

Contexte

 

Through its research into advanced materials and processes, the “Materials Research and Technology” (MRT) Department with is 170 researchers and engineers, to the emergence of enabling technologies that underpin the innovation processes of local and international industry. MRT’s activities hinge on three thematic pillars: nanomaterials and nanotechnology, sustainable composite materials and manufacturing and process technologies, including scientific instrumentation.

The department also features two high- tech platforms, one focusing on composites and one providing characterization and testing.

 

 

 

Description

 

The internship aims to develop a numerical procedure to predict damage and track its evolution within long fiber-reinforced composites (LFRC). The methodology consists of numerical simulations performed on unidirectional LFRC samples using Abaqus software. Then a machine-learning algorithm handles and treats the generated data to establish the apparition and evolution law of damage. Further, the intern applies the model to study the response of LFRC structures under conditions and scenarios that are more complicated. The intern gives a final report at the end of the study.

 


Profil

 

Education

  • Master 1 or Master 2

Competencies

  • Mechanical or civil engineering, numerical methods and CAD software are assets.

Language

  • English is required
  • Other languages are assets

 

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MRT-2020-Intern-018


 

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Contact

Dr Lyazid BOUHALA
Dr Lyazid BOUHALA

Dr Yao KOUTSAWA
Dr Yao KOUTSAWA