AREA: Digital Lifecycle Management

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AREA: Digital Lifecycle Management
Sub-area: Digital factory Modelling
Dr Kobby A Kodua, Asst. Professor of Digital Manufacturing
([email protected], 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:
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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
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