Machine learning is reshaping the way we evaluate and manage structural integrity, offering innovative solutions to address complex engineering challenges across industries. This workshop explores how cutting-edge technologies can enable the digital transformation of materials performance assessment, enhance safety, and promote sustainability across critical sectors, driving advancements across the global engineering landscape.
Structural integrity assessment and management are critical challenges across engineering sectors, with significant economic implications. These challenges are essential for optimising materials performance, extending system lifetimes, and reducing maintenance costs. This workshop builds on the successful collaboration between the University of Surrey, UK Atomic Energy Authority (UKAEA), National Physical Laboratory (NPL), and Sente Software Ltd. It highlights the seamless integration of cutting-edge materials characterisation and mechanical testing breakthroughs with a machine learning (ML)-powered modelling approach, leveraging robust data analysis algorithms to address residual stress challenges in fusion and transform structural integrity prediction. While ML holds transformative potential, challenges such as resistance to new methods and the reliance on high-quality datasets must be overcome. By bringing together leading experts across industries, the workshop will evaluate scientific achievements and societal impact through panel discussions and interviews. These advances are crucial for the sustainability and safety of future technologies, contributing to Materials 4.0, establishing a new paradigm for materials assessment and lifecycle management. The ultimate goal is to drive industry adoption of this ML-powered framework, supporting critical sectors while advancing net-zero goals and promoting sustainable industrial practices..
