Machine Learning Innovations for Structural Integrity and Net Zero Goals
This workshop will focus on reshaping methods for evaluating and managing structural integrity using machine learning technologies. Bringing together leading experts in relevant industries, and including panel discussions and interviews, it will explore how cutting-edge technologies can enable innovative solutions for digitalising materials performance assessment and promoting sustainability – driving scientific achievement and societal and economic impact.
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..
Please note that registration for this event is by invitation only. Please contact Dr Bin Zhu for further information if you are interested in this topic.
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