Application of Artificial Intelligence in Life Cycle Assessment of Composites Manufacturing (sponsored by the National Composites Centre)

Life Cycle Assessment (LCA) is a methodology increasingly used in industry for assessing the environmental impact associated with all life cycle stages of a product, process or service. Gathering comprehensive, accurate data for every stage of product life cycle can be challenging, meaning data can be incomplete thus impacting the reliability of LCA results. Moreover, identifying the best impact assessment is subjective and can affect the final result. For composite structures, these effects are further compounded by decisions made early in the design process such as: fibre and matrix selection, geometry and manufacturing processes. Artificial intelligence (AI)/Machine Learning (ML) could potentially overcome these challenges, enhancing the precision, efficiency, and depth of environmental impact assessments.

You will join a large cohort of CDT students sponsored by the National Composites Centre, which has been supporting Engineering Doctorate students for more than 10 years. Supported by this wealth of experience, you will develop AI tools capable of estimating the LCA impact of design decisions early on in the design process. The research will comprise:

• Surveying existing LCA capabilities for composites to identify gaps in current tools and data.
• Collect and homogenize primary data for composite manufacture from literature and in collaboration with OEMs. Design and implement a Universal Database Structure for LCA on composite materials.
• Build an AI framework for design decisions support. Through the use of deep learning technologies, you will make system capable of automatic parametrization of structural geometries and materials, by making use of the widest range of primary LCA data.
• Design, build and evaluate LCA-AI framework for a demonstrator structure.

Candidate Requirements

Applicants must hold/achieve a minimum a 2:1 MEng or merit at Masters level or equivalent in engineering, physics or chemistry. Applicants without a master’s qualification may be considered on an exceptional basis, provided they hold a first-class undergraduate degree. Please note, acceptance will also depend on evidence of readiness to pursue a research degree and performance at interview.

To apply please submit a personal statement, outlining your experience and why you are interested in PhD/EngD project, your CV and transcript of results to https://www.bristol.ac.uk/study/postgraduate/apply/. Please do not submit a project description; this is unnecessary as the project is already defined. Please select EngD in Composites Manufacture and enter Professor Janice Barton the Director of the CDT as the 2^nd supervisor ([email protected])