Compression and impact characterization of helical and slotted cylinder springs

Meva’a

Period. Polytech. Mech. Eng.

2018

“…The bending and torsional components M XC and M YC of the internal unknown loads X C and Y C at m(R,φ C ) are defined on ( 2 and c 2 are gathered in Table 1.…”

Section: Expressions Of Internal Displacementmentioning

confidence: 99%

“…They can secure against propagation of vibrations between mechanical systems joined parts. They are capable of absorbing loads to such a range which is impossible in the case of conventional helical springs [1,2]. Used as shock-absorbers or vibration isolator [3], they are excellent dissipators and accumulators of energy.…”

Section: Introductionmentioning

confidence: 99%

On the Stress Distribution in Slotted Cylinder Springs

Meva’a

Period. Polytech. Mech. Eng.

2018

Convergence analysis and mesh optimization of finite element analysis related to helical springs

Cadet,

Paredes

2024

Mechanics & Industry

Helical springs are widely used in engineering applications. In order to reduce cost in “try and error” time consuming experimental campaigns, numerical simulations became an essential tool for engineers. Indeed, it saves considerable time in the ahead design phase of a project to ensure the feasibility of structures. However, these simulations run thanks to a lot of parameters, which all must be selected carefully to get access to reliable results. In this paper, ten main modeling parameters are presented. Thanks to a valuable literature statistical analysis, seven of them are settled. Three remain to be studied: the mesh density, the order of the elements and the integration method. Then, three convergence analyses are performed with ABAQUS about the circular geometry accuracy of the tessellated surface, the axial stiffness (and axial load) accuracy of the helical spring and the maximal Von Mises stress accuracy within the helical spring. The numerical campaign is led with 8 mesh densities along the circumference and 6 element types. After comparison, in order to get both fast and accurate results, a limited list of near-optimal combination of density and element type are proposed. The users are free to use any of the presented solutions in function of the desired admissible accuracy of their model.The proposed meshing technique can be exploited for any helical structure with circular cross section, mainly loaded in torsion and shear, such as extension and compression springs.

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

Helisel işleme yay sabitinin belirlenmesi amacıyla parametrik korelasyon tekniğini kullanarak bir yapay sinir ağı geliştirilmesi

Sabuncuoğlu

Demirtaş

2020

 Development of artificial neural network for helical machined springs  Utilization of Parametric Correlation Technique  Confirmation of ANN results with FEA and Torsional testsDevelopment of an artificial neural network (ANN) for the determination of the spring constant of machined helical springs, which may be preferred over conventional springs due to their high performance and operating efficiency, is presented. Inıtially, finite element analyses were performed with various dimensional parameters and the obtained spring constant values were verified with the tests performed with the designed test setup. Parametric correlation analysis was performed using the confirmed finite element results and the effect of each spring dimensional parameter on the spring constant was determined. The parameters required for ANN training was determined according to the this correlation result. The spring constant results obtained from the developed ANN was compared with the finite element results confirmed by the tests and it was determined that the ANN was successful in the determination of the spring constant. The importance of parametric correlation analysis has been revealed in the development of ANN. Figure A. (Movement directions of the test setup)Purpose: The main purpose of this study is; To determine the effects of the design parameters of machining springs working under angular displacement on spring stiffness and stress by experimental and numerical methods, Verifying numerical approaches by experimental methods, To develop an artificial neural network that enables us to quickly obtain the torsion spring constant value depending on the processing spring design parameters.Theory and Methods: Torsion spring constants of different sizes of machining springs were determined by experimental and numerical methods. Results:In the results of this study; A maximum of 1.6% difference between the parameters in ANN training data set and finite element results and a maximum of 8% in intermediate values were observed. Considering the differences between the test and finite element analysis, this difference was observed to be at an acceptable level. The results obtained with the values selected outside the database set show an increase in% difference values. It is concluded that this developed ANN model is not suitable to be used for values outside the database. Conclusion:As a result of this study; An artificial neural network (ANN) based on parameter correlation was developed to determine the spring constants of the machined springs that are frequently used in the aviation industry.