The objective of this project is to develop a reliable methodology for the design for durability of mass produced mechanical components which are subject to part-to-part statistical variations.
The project has delivered a comprehensive methodology for durability and reliability design of mechanical components which span a range of metallic materials, manufacturing processes, geometries, surface finishes and a variety of modes of loading in service. The methodology and software tools encompass both high cycle fatigue (HCF), where local deformation is elastic, and low cycle fatigue (LCF), where the local deformation may be elastoplastic, regimes, thus covering a very wide range of applications, particularly in the automotive industry. In addition to the provision of numerical analytical support, provision is also made within the software system for the delivery of heuristic advice exploiting the emerging smart technologies for information management.
The industrial partners have developed exploitation plans to integrate the above into their respective inhouse design processes. The university partners will develop advanced teaching materials and consultancy services to small and medium sized enterprises (SME).
The scope of this investigation is limited to the development of probabilistic lifetime prediction methods relevant to forged and cast structural components typically used in the automotive industry. Also to be examined is the role of Knowledge-Based software techniques for the development of the above design support tools.
Fields of science
- natural sciencescomputer and information sciencessoftwaresoftware applicationssystem software
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineering
- natural sciencesmathematicspure mathematicsgeometry
- natural sciencescomputer and information sciencesartificial intelligenceheuristic programming
Topic(s)Data not available
Call for proposalData not available
Funding SchemeData not available
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