Design and analysis method of nonlinear helical springs using a combining technique : Finite element analysis, constrained Latin hypercube sampling and genetic programming

Gu, Zewen and Hou, Xiaonan and Ye, Jianqiao (2021) Design and analysis method of nonlinear helical springs using a combining technique : Finite element analysis, constrained Latin hypercube sampling and genetic programming. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 235 (22). pp. 5917-5930. ISSN 0954-4062

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Abstract

Helical springs have been widely used in various engineering applications for centuries. For many years, there is no significant development in the design methods of helical springs. Recently, a renewed interest is raised from the industry in exploring new designs for the helical springs with novel configurations due to the requirements of customised properties, such as specific spring stiffness and natural frequency for better performance of valve train systems. In this paper, an innovative method which combines the techniques of Finite Element Analysis (FEA), constrained Latin Hypercube sampling (cLHS) and Genetic Programming (GP) is developed to design and analyse helical springs with arbitrary shapes. cLHS method is applied to generate 300 sets of spring samples within a constrained design domain, and FE analysis is conducted on these spring samples. Two meta-models are developed from the 300 sets of FE results by using GP. They successfully describe the relationships between the design parameters and the overall mechanical performances including compression force and fundamental natural frequency of helical springs. The results show that the developed models have robust abilities on designing helical springs with arbitrary shapes, which significantly expands the design domain of the engineering design methods and potential for precise optimization of helical springs.

Item Type:
Journal Article
Journal or Publication Title:
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2210
Subjects:
?? spring designmachine learningcomputer-aided designdata-driven designdesign of experimentsmechanical engineering ??
ID Code:
154467
Deposited By:
Deposited On:
05 May 2021 11:50
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Jul 2024 21:37