September 2026
Microstructure-informed finite element modelling of Carbon Forged short-fibre composites: Mechanical response and piezoresistive behaviour
We are pleased to share a new scientific publication developed within the research activities of the GENMAT project:
“Microstructure-informed finite element modelling of Carbon Forged short-fibre composites: Mechanical response and piezoresistive behaviour”.
The study investigates Carbon Forged short-fibre composites, combining experimental characterisation, X-ray micro-computed tomography, Digital Image Correlation and finite element modelling to capture the influence of the material mesostructure on its mechanical response.
The work also explores the piezoresistive behaviour of Carbon Forged materials and their potential for future self-sensing and Structural Health Monitoring applications. Within GENMAT, the developed model will be exploited to generate physics-based simulation data to support the development, training and validation of data-driven and AI-based approaches.
🔗 Read the open-access article: https://doi.org/10.1016/j.compstruct.2026.120701
Published in Composite Structures, 395 (2026), 120701.
Authors: Emanuele Mattei, Alberto Ciampaglia, Raffaele Ciardiello and Davide Salvatore Paolino — Politecnico di Torino.
This research contributes to the activities of the GENMAT project, funded by the European Union under the Horizon Europe programme.
“Microstructure-informed finite element modelling of Carbon Forged short-fibre composites: Mechanical response and piezoresistive behaviour”.
The study investigates Carbon Forged short-fibre composites, combining experimental characterisation, X-ray micro-computed tomography, Digital Image Correlation and finite element modelling to capture the influence of the material mesostructure on its mechanical response.
The work also explores the piezoresistive behaviour of Carbon Forged materials and their potential for future self-sensing and Structural Health Monitoring applications. Within GENMAT, the developed model will be exploited to generate physics-based simulation data to support the development, training and validation of data-driven and AI-based approaches.
🔗 Read the open-access article: https://doi.org/10.1016/j.compstruct.2026.120701
Published in Composite Structures, 395 (2026), 120701.
Authors: Emanuele Mattei, Alberto Ciampaglia, Raffaele Ciardiello and Davide Salvatore Paolino — Politecnico di Torino.
This research contributes to the activities of the GENMAT project, funded by the European Union under the Horizon Europe programme.