Generative AI and digital twinning for the simulation of ductile/fragile fracture in segregated steel

 

 

Résilience des aciers de forge ségrégés. Etudes expérimentales et numériques réalisées dans le cadre du projet French Fab piloté par Anne-Françoise Gourgues.

 

Space-time adaptive ALM simulation

 

 

PhD thesis 2021-2024 @ Mines ParisTech / PSL University. Simulation numérique avancée pour la compréhension des points bas en résilience des aciers forgés

 

https://www.mat.minesparis.psl.eu/Recrutements/Theses/Details/Simulation-numA-copy-rique-avancA-copy-e-pour-la-comprA-copy-hension-des-points-bas-en-rA-copy-silience-des-aciers-forgA-copy-s/37111

Stage EDF/Mines/AFH (Paris region) Advanced data assimilation framework for WAAM additive layer manufacturing processes

EDF is currently investigating the capabilities of emerging additive layer manufacturing technologies such as WAAM (wire + arc additive manufacturing). This novel manufacturing process leverages existing welding technologies, whilst promising to allow engineers to build or repair large engineering components in a flexible and reliable manner. As of today, this process is not mature enough to be used for industrial production. This project focusses on establishing a robust numerical pipeline between numerical simulation of WAAM processes on the one hand, and data-rich lab experiments on the other hand. This pipeline will help researchers advance current understanding and control capabilities of this emerging class of additive manufacturing processes. 
 

One of the major difficulties limiting the capabilities of today’s numerical simulators is the multiscale and multiphysics nature of additive manufacturing processes, and WAAM in particular. Predicting how the shape of manufactured parts deviates from nominal geometries proves incredibly challenging, as fine-scale couplings between electromagnetics, thermodynamics, fluid and solid mechanics need to be resolved over large spatial domains and long periods of time. To make simulations possible, it is usually proposed to adopt a simplified, thermo-mechanical macroscopic point of view. However, in order to take unrepresented physics into account, model inputs (heat source models, material deposition models, ...) need to be reliably inferred from appropriately generated experimental data.

The project aims to establish a cutting-edge two-ways experiment-to-simulation pipeline to improve and automatise this inference process. Today’s labs are equipped with high-resolution scanners that may be used to acquire the full geometry of built objects. In turn, we wish to calibrate EDF’s thermo- mechanical model so that the predicted shape deviation from CAD matches that observed in the real-world. It will then be possible to virtually predict the shape deviation from ACD for a new process or component, without manufacturing it physically, thereby paving the way towards virtual design and optimisation of ALM operations.

The technical outlines of the project are as follows.

•   The candidate will construct geometrical algorithms to assimilate point cloud data generated 
•   by 3D scanning of manufactured parts, i.e. to allow inference algorithms to compare real surface profiles to simulated ones. The algorithms will be developed in Python and subsequently interfaced with EDF’s solid mechanics finite element code code_aster. 
• The candidate will develop robust data-assimilation algorithms to tune/learn simplified computational models (of inherent-strain type) based on the 3D-scan data available at EDF. The procedure will be validated against its ability to blindly predict the shape of new WAAM products. 
• The candidate will deploy a data mining strategy to improve the transferability of the calibrated model parameters over a range of manufacturing conditions and part geometries.

The work will be hosted by Mines ParisTech (Centres des Matériaux, http://www.mat.mines- paristech.fr/Research/Scientific-clusters/SIMS/ ), and in partnership with EDF Chatou. The duration of the stage is 6 months minimum, up to 9 months (expected start: winter/spring 2021). The candidate may take part in designing new sets of experiments as part of the project. The work is sponsored by the Additive Factory Hub (AFH), a group of high-tech industries teaming up to advance the state-of-the-art in metal additive layer manufacturing through shared research. The candidate is expected to take an active part in the dissemination of the results in the AFH network. https://www.additivefactoryhub.com/.

Requirements:

• Proven experience in computational engineering & numerical simulation - Strong interest in manufacturing and digital twining 
• Interest in machine learning and data mining 
• Excellent analytical skills   
• Scientific curiosity and strong interest in digital industry

Application and additional enquires:

Send CV and statement of motivation to Pierre Kerfriden, Mines ParisTech pierre.kerfriden@mines-paristech.fr
CC: Sami Hilal, EDF Chatou, sami.hilal@edf.fr , Djamel Missoum-Benziane, djamel.missoum-benziane@mines-paristech.fr 

 

 

 

Keywords:  

Additive Layer Manufacturing, Computational Engineering, Applied Mathematics, Finite Element Method, Data Assimilation, Machine Learning, Industry 4.0

 

https://calcul.math.cnrs.fr/job_080ea3e05f8bd8bbf8160c92894af850.html

Injection Stretch-Blow Molding of thin-walled PET containers

Manufacturing the Yorkshire Pudding

Simulation of additive layer manufacturing by direct energy deposition using CutFEM

 

 

S. Claus, S. Bigot and P. Kerfriden, CutFEM Method for Stefan--Signorini Problems with Application in Pulsed Laser Ablation, SIAM J. Sci. Comput., 40(5), 2018
 

https://doi.org/10.1137/18M1185697

Eddie is back

Additive Layer Manufacturing simulations (metal deposition) with CutFEM

Simulation of micro-EDM die-sinking

Crater-by-crater geometric simulations of micro-EDM using fast Octree computations. Results from the PhD thesis of Anthony Surleraux. Supervision by S. Bigot and P. Kerfriden

http://orca.cf.ac.uk/80776/1/2015SurlerauxABPhD.pdf

Laser ablation: micro-cavity

We have developed a cut finite element method for one-phase Stefan problems, with applications in laser manufacturing. The geometry of the workpiece is represented implicitly via a level set function. Material above the melting/vaporisation temperature is represented by a fictitious gas phase. The moving interface between the workpiece and the fictitious gas phase may cut arbitrarily through the elements of the finite element mesh, which remains fixed throughout the simulation, thereby circumventing the need for cumbersome remeshing operations. The primal/dual formulation of the linear one-phase Stefan problem is recast into a primal non-linear formulation using a Nitsche-type approach, which avoids the difficulty of constructing inf-sup stable primal/dual pairs. Through the careful derivation of stabilisation terms, we show that the proposed Stefan-Signorini-Nitsche CutFEM method remains stable independently of the cut location. In addition, we obtain optimal convergence with respect to space and time refinement. Several 2D and 3D examples are proposed, highlighting the robustness and flexibility of the algorithm, together with its relevance to the field of micro-manufacturing.

 

Simulations by S. Claus, S. Bigot and P. Kerfriden in the FEniCS library CutFEM.

S. Claus, S. Bigot and P. Kerfriden, CutFEM Method for Stefan--Signorini Problems with Application in Pulsed Laser Ablation, SIAM J. Sci. Comput., 40(5), 2018
Funding: Sêr Cymru National Research Network

Thermal ablation simulation over a fixed background mesh

Quasi-static simulation of a thermal ablation manufacturing process by pulse laser using the CutFEM technology. Modelling and simulation by Dr Claus & Dr Kerfriden in collaboration with Dr Bigot