Multi-objective optimization of sound package parameters for interior high frequency noise of heavy-duty truck using grey theory

Chen, Songhang; Chen, G; Wang, D; Song, Jiqang

doi:10.1007/s12239-015-0097-z

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…Thus, reducing the cab interior noise for heavy commercial vehicles is necessary. 4,5 Cab interior noise includes airborne and structureborne noises. Airborne noise is the external noise of the cab transferring into the cab through air channels and is mainly composed of high-frequency noises.…”

Section: Introductionmentioning

confidence: 99%

Vibration source identification of a heavy commercial vehicle cab based on operational transfer path analysis

Zhang

Pan

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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Operational transfer path analysis is applied in this study to identify the vibration source and its critical transfer path. A simple analytical five-degrees-of-freedom mechanical isolation system is first taken as an example to illustrate the analysis flow and to validate the accuracy of operational transfer path analysis. The acceleration amplitude spectrum of the receiver is used to prove the accuracy, and the path contribution of each path is used to identify the critical path. Operational transfer path analysis is then applied to the cab mount system of a heavy commercial vehicle to identify the vibration source and its critical transfer path. The vibration energy propagation capabilities from the four cab mounts to the driver’s seat are analyzed by operational transfer path analysis with the path contribution analysis, and the maximum vibration source is identified by the path operation contribution analysis. The analysis and evaluation method of the operational transfer path analysis introduced in this study can provide a research foundation and reference for vibration or noise source identification in mechanical systems.

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Section: Introductionmentioning

confidence: 99%

Vibration source identification of a heavy commercial vehicle cab based on operational transfer path analysis

Zhang

Pan

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…proposed and implemented fuzzy goal programming models to optimize the performance of rigid PU foam for four quality characteristics. Chen et al . applied gray rational analysis (GRA) with a Taguchi method to optimize the acoustic performances of the sound package.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Al-Refaie et al 23 proposed and implemented fuzzy goal programming models to optimize the performance of rigid PU foam for four quality characteristics. Chen et al 24,25 applied gray rational analysis (GRA) with a Taguchi method to optimize the acoustic performances of the sound package. He et al 26 utilized the GRA method and multiobjective particle swarm optimization algorithm to optimize the acoustic package materials of firewall and floor.…”

Section: Introductionmentioning

confidence: 99%

Optimization of acoustic performances of a new tung oleic acid‐based composite polyurethane foam

Chen

2019

J of Applied Polymer Sci

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Bio‐based polyurethane (PU) foam, different from traditional PU foam, is a new green environmental acoustic material. It can be used in the field of automobiles. In this work, a new tung oleic acid‐based composite PU (TOAPU) foam was prepared using tung oleic acid‐based polyol and polyether polyols 3630. It is important to optimize the formulation of TOAPU foam for better acoustic performance. The effect of each component on the acoustic performance of TOAPU is ordered by screening test. A1, silicone, and A33, the three components that have the greatest impact on the acoustic performance of TOAPU, are used as variables. The response surface method is used to create the model of the effects of different components on the acoustic properties of TOAPU. The model was optimized by using nondominated sorting genetic algorithm II method. The optimized acoustic performance property of TOAPU foams was synthesized by adding 0.27 g of A1, 1.03 g of A33, and 2.81 g of silicone oil. The experimental results show that the mean sound absorption coefficient and transmission loss can reach 0.515 and 21.389 dB. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47861.

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“…Jeon et al [12] used particle swarm optimization (PSO) algorithm for optimal bending design of vibrating plate to minimize noise radiation. Chen et al [13] applied the grey rational analysis (GRA) with Taguchi method to optimize the acoustic performances of the sound package. Jiang et al [14] employed the Taguchi method base on orthogonal arrays to conduct the experiments to improve the acoustic behaviors of PU foams.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Acoustic Performances of Polyurethane Foam Composites

2018

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Polyurethane (PU) foams are widely used as acoustic package materials to eliminate vehicle interior noise. Therefore, it is important to improve the acoustic performances of PU foams. In this paper, the grey relational analysis (GRA) method and multi-objective particle swarm optimization (MOPSO) algorithm are applied to improve the acoustic performances of PU foam composites. The average sound absorption coefficient and average transmission loss are set as optimization objectives. The hardness and content of Ethylene Propylene Diene Monomer (EPDM) and the content of deionized water and modified isocyanate (MDI) are selected as design variables. The optimization process of GRA method is based on the orthogonal arrays L9(34), and the MOPSO algorithm is based on the Response Surface (RS) surrogate model. The results show that the acoustic performances of PU foam composites can be improved by optimizing the synthetic formula. Meanwhile, the results that were obtained by GRA method show the degree of influence of the four design variables on the optimization objectives, and the results obtained by MOPSO algorithm show the specific effects of the four design variables on the optimization objectives. Moreover, according to the confirmation experiment, the optimal synthetic formula is obtained by MOPSO algorithm when the weight coefficient of the two objectives set as 0.5.

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Multi-objective optimization of sound package parameters for interior high frequency noise of heavy-duty truck using grey theory

Cited by 4 publications

References 29 publications

Vibration source identification of a heavy commercial vehicle cab based on operational transfer path analysis

Vibration source identification of a heavy commercial vehicle cab based on operational transfer path analysis

Optimization of acoustic performances of a new tung oleic acid‐based composite polyurethane foam

Multi-Objective Optimization of Acoustic Performances of Polyurethane Foam Composites

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