Title: Physically-Informed Neural Networks for residual stress distribution assessment applied to high-temperature industrial manufacturing processes
Date: 2021-12-20 13:08
Slug: job_b4b47ee7bfbc3d8fe051c0070d5c158a
Category: job
Authors: David iampietro
Email: david.iampietro@edf.fr
Job_Type: Stage
Tags: stage
Template: job_offer
Job_Location: Chatou
Job_Duration: 6-9 mois
Job_Website:
Job_Employer: EDF Lab Chatou
Expiration_Date: 2022-03-14
Attachment: job_b4b47ee7bfbc3d8fe051c0070d5c158a_attachment.pdf
Keywords: Computational Engineering, Applied Mathematics, Residual Stress, Welding, Finite Element Analysis, Data Assimilation, Machine Learning, Deep Learning, Neural Network, Industry 4.0
EDF is currently investigating the capabilities of emerging data-driven solutions to assess residual stress distribution induced by high-temperature manufacturing processes.
The objective of the internship is to design a neural network able to predict the residual stress tensor field within a given welded body considering the spatial and temporal evolution of temperature as input.