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

Introduction

It appears paradoxical how today's frontier & high-impact research seeks to design 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.

Project Aim

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.

Methodology

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.

Previous Work

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.

Experimental Campaign

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.

Model Development

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.

Confirmation of Capabilities

The outstanding capabilities of the framework developed in BREAKDOWN will be confirmed through specific demonstrators.

Conclusion

BREAKDOWN will excellently contribute towards the development of a much more sustainable design procedure with unprecedented social, economic, and environmental benefits.