Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Design and analysis method of nonlinear helical springs using a combining technique
T2 - Finite element analysis, constrained Latin hypercube sampling and genetic programming
AU - Gu, Zewen
AU - Hou, Xiaonan
AU - Ye, Jianqiao
PY - 2021/11/1
Y1 - 2021/11/1
N2 - 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.
AB - 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.
KW - Spring design
KW - machine learning
KW - computer-aided design
KW - data-driven design
KW - design of experiments
U2 - 10.1177/09544062211010210
DO - 10.1177/09544062211010210
M3 - Journal article
VL - 235
SP - 5917
EP - 5930
JO - Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
JF - Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
SN - 0954-4062
IS - 22
ER -