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Wide-ranging Probabilistic Physics-guided Machine Learning Approach to Break Down the Limits of Current Fatigue Predictive Tools for Metals

Project description

Studying structural performance of materials at the microscopic scale

In engineering, material fatigue refers to the deterioration over time due to repeated loading. Even if the applied stress is below the material's ultimate tensile strength, repeated cycles of loading and unloading can lead to the formation of microscopic cracks that grow progressively with each cycle, leading to fracture or failure. The ERC-funded BREAKDOWN project aims to transform current engineering design methods by focusing on material properties at the microscopic scale. The research team will develop new models and experiments to characterise material fatigue, paving the way to more reliable and sustainable engineering designs.

Objective

It appears paradoxical how today's frontier & high-impact research seeks for designing new materials to delay structural failures – especially fatigue – while the same effort is not seen concerning the way materials can be efficiently and safely used in real structural applications. BREAKDOWN aims to transform engineering products’ design methods by identifying and including a set of (sub)micro-scale material inhomogeneities characteristics in a novel probabilistic framework. The time has come to exploit modern experimental techniques to probe material properties at a small scale, which are scarcely involved in current fatigue characterisation schemes. To attain this very ambitious goal, the project will rely on a breakdown of different classes of inhomogeneities to advance the fundamental mechanical understanding of their contribution to fatigue, and then reunite them within an advanced Bayesian Physics-Guided Neural Network (B-PGNN) frame. Over the past three years, I assiduously worked to prove the feasibility of BREAKDOWN and demonstrate its superior capabilities. However, I have merely scratched the surface of what is potentially achievable with this approach, both in terms of knowledge advancement and real engineering applications. An extensive multimodal experimental characterisation campaign will be conducted on different material inhomogeneity states to separate and identify their individual influence on fatigue in a systematic and detailed way. Cutting-edge numerical & analytical models will be developed and exploited as the physics knowledge in the B-PGNN scheme to effectively tackle the small datasets issue when dealing with fatigue and to ensure soundness of results. The outstanding capabilities of the framework developed in BREAKDOWN will be confirmed through specific demonstrators. BREAKDOWN will excellently contribute towards the development of a much more sustainable design procedure with unprecedented social, economic and environmental benefits.

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2024-STG

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Host institution

UNIVERSITA DEGLI STUDI DI UDINE
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 499 954,00
Address
VIA PALLADIO 8
33100 Udine
Italy

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Region
Nord-Est Friuli-Venezia Giulia Udine
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 499 954,00

Beneficiaries (1)

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