Parabolic Leaf Spring Optimization and Fatigue Strength Evaluation on the Base of Road Load Data, Endurance Rig Tests and Non Linear Finite Element Analysis

Kanbolat, Ahmet; Soner, Murathan; Erdogus, Tolga; Karaagac, Mustafa

doi:10.4271/2011-01-0438

Cited by 10 publications

(8 citation statements)

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“…Through the integration of computers, manufacturing can be faster and less error-prone, although the main advantage is the ability to create automated manufacturing processes. CIM is the manufacturing approach of using computers to control the entire production process [6] …”

Section: Review Of Literaturementioning

confidence: 99%

Analytical Study on Integrated Optimization Quality in Computer Manufacturing

2017

IJMTER

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Section: Review Of Literaturementioning

confidence: 99%

Analytical Study on Integrated Optimization Quality in Computer Manufacturing

2017

IJMTER

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“…To predict the fatigue failure of a parabolic leaf spring, the finite element analysis (FEA) is widely used to simulate the applied stress distribution and fatigue behaviour in the design stage. [14][15][16][17][18][19][20] Nevertheless, the deterministic and probabilistic optimisation of leaf spring design parameters, which need a large number of stress model calls, [21][22][23] involve a considerable computational effort by using a FEM of parabolic leaf spring (PLS). Therefore, it is very important to use a simple and precise model to carry out the applied stress distribution.…”

Section: Introductionmentioning

confidence: 99%

A simple analytical bending stress model of parabolic leaf spring

Atig

Sghaier

Seddik

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part C:

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The evaluation of stress distribution, produced by vertical loading along a parabolic leaf spring, presents an essential aspect during the design stage. Commonly, designers utilize the finite element analysis to simulate the stress behaviour of a parabolic leaf spring. Nevertheless, the use of such method is a time-consuming process during the deterministic and the reliability-based fatigue design optimisation. In this study, we propose three analytical models describing the bending stress distribution of a simply supported single asymmetric parabolic leaf spring: (i) an initially curved single asymmetric parabolic leaf spring, subjected to a concentrated load; (ii) a straight single asymmetric parabolic leaf spring, subjected to a uniform load and (iii) an initially curved single asymmetric parabolic leaf spring, subjected to a uniform load. Bending stress distribution results of classical, finite element and proposed models are compared for several case studies. It is observed that the third model is the most precise model compared to the finite element analysis of single asymmetric parabolic leaf spring. Therefore, the suggested model can be used to generate fatigue life diagram that predicts the required mean and alternating load values for a desired fatigue life with an acceptable accuracy and a reduced computational time.

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“…He used particle swarm optimization algorithm (PSO) to solve the model, and the optimized design parameters were proposed with a view to minimize weight and stress in the main and auxiliary leaf spring. Kanbolat Ahmet et al [13] proposed a more precise method based on nonlinear finite element solutions by evaluating the effects of the processing factors, the geometrical tolerances and the variations in the material characteristics. He proposed a hybrid method, between the traditional and the recent ones, which correlates the real-life conditions and the results of computer-aided engineering.…”

Section: Introductionmentioning

confidence: 99%

“…It is available in the open literature that the analytical methods for fatigue life prediction provide results closer to the experimental results. Therefore, these analytical methods are proposed as an alternate to testing [4,[11][12][13][14][15][16][17][18][19]. The material replacement has been considered for weight reduction [21], but the effect on fatigue life has to be accounted.…”

Section: Introductionmentioning

confidence: 99%