Effective utilizing axial nonlinear characteristics of diaphragm spring and waveform plate to enhance breakaway performances of a clutch

Shangguan, Wen‐Bin; Liu, Xuelai; Rakheja, Subhash; Hou, Qiufeng

doi:10.1016/j.ymssp.2018.05.060

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…This iterative process entails recurrent adjustments to models. However, this iterative approach proves inadequate for achieving the objectives of expeditious analysis and falls short of meeting the accuracy requisites intrinsic to multi-objective optimization designs for multiple mounts within the powertrain mounting system [14][15][16]. Therefore, it is necessary to seek a design method with higher efficiency and more functions.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Electric Vehicle Powertrain Mounting System Optimization Based on Cross-Industry Standard Process for Data Mining

Wu,

Zhao,

Peng

et al. 2024

Electronics

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The meticulously engineered powertrain mounting system of hybrid electric vehicles plays a critical role in minimizing vehicle vibrations and noise, thereby enhancing the longevity of vital powertrain components. However, developing and designing such a system demands substantial time and financial investments due to intricate analysis and modeling requirements. To tackle this challenge, this study integrates data mining technology into the design and optimization processes of the powertrain mount system. The research focuses on the powertrain mounting system of a transverse four-cylinder hybrid electric vehicle, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to establish a data-mining prediction model for mounting stiffness. This model utilizes three data mining algorithms—Multi-SVR, MRTs, and MLPR—to assess their predictive accuracy concerning mounting system stiffness estimation. A comparative analysis reveals that the MRTs algorithm outperforms others as the most effective prediction model. The proposed predictive model elucidates the quantifiable correlation between vibration isolation performance and installation stiffness, overcoming complexities associated with traditional modeling approaches. Applying this model in powertrain mounting system design showcases the efficacy of the CRISP-DM-based approach, significantly enhancing design efficiency without compromising prediction accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Hybrid Electric Vehicle Powertrain Mounting System Optimization Based on Cross-Industry Standard Process for Data Mining

Wu,

Zhao,

Peng

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…This iterative process entails recurrent adjustments to the multibody dynamics model and mathematical theoretical model. However, this iterative approach proves inadequate for achieving the objectives of expeditious analysis and falls short of meeting the accuracy requisites intrinsic to multi-objective optimization designs for multiple mounts within the powertrain mounting system [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Hybrid-electric Vehicle Powertrain Mounting System Optimization Based on Cross-industry Standard Process for Data Mining

Wu,

Zhao,

Peng

et al. 2024

Preprint

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The meticulously engineered powertrain mounting system of hybrid-electric vehicles plays a critical role in minimizing vehicle vibrations and noise, thereby enhancing the longevity of vital powertrain components. However, developing and designing such a system demands substantial time and financial investments due to intricate analysis and modeling requirements. To tackle this challenge, this study integrates data-mining technology into the design and optimization processes of the powertrain mount system. The research focuses on the powertrain mounting system of a transverse four-cylinder hybrid-electric vehicle, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to establish a data-mining prediction model for mounting stiffness. This model utilizes three data mining algorithms—Multi-SVR, MRTs, and MLPR—assessing their predictive accuracy concerning mounting system stiffness estimation. A comparative analysis reveals that the MRTs algorithm outperforms others as the most effective prediction model. The proposed predictive model elucidates the quantifiable correlation between vibration isolation performance and installation stiffness, overcoming complexities associated with traditional modeling approaches. Applying this model in powertrain mounting system design showcases the efficacy of the CRISP-DM-based approach, significantly enhancing design efficiency without compromising prediction accuracy.

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“…The static behavior of disk springs and slotted disk springs is a subject of long-standing concern in engineering with both practical importance and scientific fascination [1][2][3][4]. The most common applications for disk springs are clutches [5][6][7], sliding hinge joints [8][9][10], vibration absorbers [11][12][13], energy harvesters [14,15], and rocking bridge piers [16]. Compared with conical disk springs, slotted disk springs, also known as diaphragm springs, have a larger deflection and a lower stiffness given their regularly arranged slots [17,18].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Testing for Slotted Disk Springs Considering Linearly Gradient Thickness and Friction

Du,

Sun

2023

Applied Sciences

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The slotted disk spring has been widely used given its nonlinear characteristics. However, the number of studies on them is limited, and the influences of nonuniform thickness have not been investigated theoretically and experimentally. Additionally, the hysteresis caused by friction has been neglected in most studies. By introducing the symmetric friction condition and the dimensionless thickness variation parameter, the present study establishes a mathematical model for slotted disk springs with linearly graded thickness. By treating the slotted segment as a number of cantilever beams with both a linearly gradient thickness and width, a new analytical formula is developed to characterize the load–deflection of slotted disk springs based on the moment equilibrium equation. The proposed model allows the direct quantification of the influences of the thickness variation parameter and the symmetric friction condition on the load–deflection characteristics. To validate the proposed model, slotted disk springs with different parameter configurations are designed and tested. Comparisons between measured and theoretical results illustrate that the proposed model can effectively describe the load–deflection characteristics of slotted disk springs with both uniform thickness and linearly gradient thickness. Due to the hypothetic linear variable width of slotted segments, the proposed model has a better performance in predicting slotted disk springs with rectangular slots.

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Effective utilizing axial nonlinear characteristics of diaphragm spring and waveform plate to enhance breakaway performances of a clutch

Cited by 12 publications

References 14 publications

Hybrid Electric Vehicle Powertrain Mounting System Optimization Based on Cross-Industry Standard Process for Data Mining

Hybrid Electric Vehicle Powertrain Mounting System Optimization Based on Cross-Industry Standard Process for Data Mining

Hybrid-electric Vehicle Powertrain Mounting System Optimization Based on Cross-industry Standard Process for Data Mining

Modeling and Testing for Slotted Disk Springs Considering Linearly Gradient Thickness and Friction

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