AREA: Digital Lifecycle Management Sub-area: Digital factory Modelling Dr Kobby A Kodua, Asst. Professor of Digital Manufacturing (k.agyapong-kodua@warwick.ac.uk, Tel: +44 (0)24 7657 4259) PhD Research topic: Early stage life cycle prediction and assessment of parts, machines and systems behaviour DESCRIPTION: Concurrent Engineering and other modern manufacturing paradigms have enabled closer collaboration between stakeholders in the life cycle management of product knowledge. Results from these paradigms have allowed various degrees of iterations between, particularly, designers and manufacturers. To reduce the number of iterations and also enable robust product concept formulation, designers would need to be supported with rigorous mathematical and semantic tools with embedded capability to analyse and predict parts and systems performances and behaviour at various stages of their life cycles. A highly motivated PhD student is required to develop new methodologies and systems technologies which will support the early stage assessment of product performance. These would typically be applied at concept generation and evaluation stages so that metrics for selecting suitable concepts are based on predicted real life performances. Focus therefore will be made on mathematical and complex systems models which are able to determine: 1) Stiffness and damping of mechanical parts and assemblies 2) Accuracy, reliability and maintainability of parts and assemblies 3) Overall Equipment Effectiveness (OEE) and Life Cycle Cost (LCC) of parts and assemblies This research will lead to the development of: 1. Semantically rich versatile plug-in systems architecture to support the assessment of parts and assemblies at early stage of design proposals. 2. Mathematical and systems models for early stage estimation of key performance indicators such as stiffness, damping, LCC, OEE, etc. 3. Metrics and techniques for capturing, updating and feeding back knowledge from operational phases to the design phase Requirements: Candidates must have a very good degree in one of the following fields: Mechanical, Production, Manufacturing, Industrial or Systems Engineering; Operations Management; Decision Sciences; Computer Science/Engineering. Candidates must be self-motivated, possess excellent communication skills, an ability to work in teams and adhere to project deadlines. The candidate is expected to develop analytical, visual and computational skills and submit results of work for publications in academic journals. An original contribution to knowledge in the field is expected. Candidates with some knowledge or experience in any of these: Artificial Intelligence, Ontologies and Semantic Technologies, Databases, web-GUI development, Programming languages (C++, Java, Visual Basic), are encouraged to apply