KnE Engineering
ISSN: 2518-6841
The latest conference proceedings on all fields of engineering.
Shape optimisation of a snowboard binding highback. A case study of generative design process comparison
Published date:Feb 09 2017
Journal Title: KnE Engineering
Issue title: The International Conference on Design and Technology
Pages:73-83
Authors:
Abstract:
FEA software is traditionally expensive to purchase, takes a high level of technical skill and understanding and requires users to dedicate years to develop specialist skills. With the increasing popularity of more user-friendly, elementary software packages such as Fusion360, more cost effective and efficient processes can be developed and harnessed, especially by SME’s and designers that don’t have the ability to purchase expensive software packages. One particular FEA element that has recently begun transitioning from highly specialised to more readily available is ‘generative design’ and ‘shape optimisation.' Shape optimisation has only been able to be utilised by large corporations with large research and development budgets. This case study looks at exploring and optimising the methods involved in generative design for product development and it’s aimed at facilitating practises for small to medium enterprises (SME’s).
The work described in this paper presents a study using a snowboard binding highback component which was reverse engineered using 3D scanning. A blank model, free of any discerning features was created from the scan and then used as the platform for the generative design phase. This process was completed using easily accessible software (Fusion 360) as well as high-end professional software (Ansys 16). A comparison between the two workflows analyses the resultant model outcomes and outlines efficiencies regarding processing time, technical skill, and latent difficulties of the entry-level process for generative design of the snowboarding high back.
This paper aims to demonstrate and describe an optimisation model for generative design and shape optimisation during entry-level product development.
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