Description :
GENEX aims at developing a novel end-to-end digital twin-driven framework based on enhanced computational models, which embed the interdisciplinary knowledge of the aircraft components and the manufacturing/repairing processes, to support the optimized manufacturing of composites parts, enable the continuous operation of aircrafts and improve the composites repairing processes for ensuring aircraft’s safety and airworthiness. ATL (automated tape laying) process coupled with THz-based in-process monitoring together with hybrid-twin simulation methods will be developed for eco-efficient and advance manufacturing of innovative reprocessable-repairable-recyclable (3R)-resin-and state-of-the-art thermoplastic composites. Then innovative data and physics-based machine learning algorithms for damage detection and location combined with advanced high-performance computing (HPC)-based multi-physics and artificial intelligent-powered digital twin tools for fatigue life prediction, will be implemented to transform information from optimized onboard piezoresistive sensors data networks interfaced with low-power wireless communication platform to health and usage assessment and prognosis. Augmented reality tools together with novel laser-assisted methods for surface cleaning and monitoring, smart monitoring and in-situ tailored heating of composite repair blankets will be further developed to provide additional assistance in manual scarf repair operations, increasing reliability of repair process, while supporting the modification and virtual certification practices. Thus, a novel digital twin-driven framework will be implemented into a common IIoT platform to integrate the developed models and data acquired, providing bidirectional dataflow, and enabling the implementation of a holistic and comprehensive data management methodology ensuring to adequately create capture, share, and reuse knowledge along the entire aircraft lifecycle. Research will consist in the advanced modelling of the evolution of curing for thermosets (TS) and degree of crystallinity for thermoplastics (TP) during the lay-up of CF/3R-resin tapes or TP-tapes for in-situ consolidation.
For TS different aspects should be considered : i) description and classification of 3R-resin tapes materials parameters ; (ii) high-resolution time-dependent thermal evaluation of 3R-resin tape, taking into account the time-evolution of viscosity and the molecular diffusion during the consolidation ; iii) thermo-kinetic model of 3R-resin during consolidation ; iv) generation of a parametric solution of curing process during in-situ consolidation by ATL by considering as input parameters the ones characterizing the 3R-resin material as well as the process parameters ; (iv) artificial intelligence based modelling performing (a) supervised classification able to inter depending on the 3R-resin material at hand, the adequate process parameters to ensure part quality and (b) advanced nonlinear regression for linking input material and process parameters to product properties and performances ; and (v) in view of all the referred points, propose optimization and control methodologies.
For TP we will proceed as follows : i) description and classification of the materials parameters of TP tapes ; (ii) high-resolution time-dependent thermal evaluation of tape, considering the heating/cooling with phase transformation and the molecular diffusion during the consolidation ; iii) crystallization model ; iv) generation of a parametric solution of the whole thermal process during in-situ consolidation by considering as input parameters the ones characterizing the tape as well as the process parameters ; (iv) artificial intelligence based modelling performing (a) supervised classification to ensure part quality and (b) advanced nonlinear regression for linking input material and process parameters to product properties and performances ; and (v) proposal of optimization and control methodologies.
The degree of curing of the composite plies will be measured by using DSC method. A similar experimental work will be performed for the thermoplastic tapes.

Activities :
• Building multi-physics models
• Collecting experimental data
• Laboratory characterization
• Use of learning techniques for models extraction and data classification
• Dissemination of scientific and technical information
• Valorization of scientific and technical research results

Keywords :
Polymers, modeling, simulation, artificial intelligence

Objectives :
Objective is to build modeling to control the shaping of polymers and composites

Candidate profile :

  M.Eng, PhD

  Languages : English and French

  Knowledge in modeling, simulation and programming

  Polymers

Reference : GENEX

Date de démarrage : 01 septembre 2023

Durée : CDD

Contacter :
Laboratoire PIMM
Jorge Peixinho
151 boulevard de l’Hôpital, 75013 Paris
email : jorge.peixinho@ensam.eu

Page web : https://pimm.artsetmetiers.fr