Probabilistic Design of Composite Wheel Spanner
Journal Title: Machine Design - Year 2017, Vol 9, Issue 2
Abstract
Probabilistic design offers tools for making reliable decisions with the consideration of uncertainty associated with design parameters and simulation models. This project discusses probabilistic design and its application into design of a composite wheel spanner for Toyota Camry cars with five (5) lug nuts. The project work is aimed at taking advantage of probabilistic design system approach over deterministic (traditional) approach to create an optimised design model of an existing composite wheel spanner. This new approach is to implement changes on a controlled, verifiable basis and deals majorly with the operating stress and material strength. The pre-existing design was rigid, robust and was only designed for a Volkswagen Beetle car with four (4) lug nuts while the new design is compatible with the Toyota Camry and all cars with 60.1mm Hub/Centre Bore, 14”-16” wheel size and 5 x114.3mm bolt pattern. The design was drawn using SolidWorks design software and various design parameters were considered. The completed design was imported from SolidWorks to ANSYS software by converting the design into a Para solid which was then simulated by varying the speed of the shaft and the material in order to get the corresponding stress analysis. The rotational speed of the shaft was varied with different gear materials on ANSYS probabilistic design system software. The result shows that the lowest amount of stress was experienced when 7079 Aluminum Alloy (ρ = 2700kg/m3) was tested at 100 rpm but Magnesium Alloy (ρ = 1700kg/m3) at 100rpm gave a higher minimum operating stress value. In this project, a 5-nut composite wheel spanner was successfully designed using probabilistic design approach. The pre-existing composite spanner was improved upon using SolidWorks and ANSYS software to design and analyse respectively. The new design is very flexible, adjustable and easy to carry. It can be adopted by car manufacturing companies.
Authors and Affiliations
Emmanuel SIMOLOWO, Michael MOSAKU
Keywords
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- EP ID EP319638
- DOI 10.24867/MD.9.2017.2.73-80
- Views 103
- Downloads 0
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