Research on sound quality of roller chain transmission system based on multi-source transfer learning.

To establish the sound quality evaluation model of roller chain transmission system, we collect the running noise under different working conditions. After the noise samples are preprocessed, a group of experienced testers are organized to evaluate them subjectively. Mel frequency cepstral coefficient (MFCC) of each noise sample is calculated, and the MFCC feature map is used as an objective evaluation. Combining with the subjective and objective evaluation results of the roller chain system noise, we can get the original dataset of its sound quality research. However, the number of high-quality noise samples is relatively small. Based on the sound quality research of various chain transmission systems, a novel method called multi-source transfer learning convolutional neural network (MSTL-CNN) is proposed. By transferring knowledge from multiple source tasks to target task, the difficulty of small sample sound quality prediction is solved. Compared with the problem that single source task transfer learning has too much error on some samples, MSTL-CNN can give full play to the advantages of all transfer learning models. The results also show that the MSTL-CNN proposed in this paper is significantly better than the traditional sound quality evaluation methods.

Synthesis and Acoustic Study of a New Tung Oil-Based Polyurethane Composite Foam with the Addition of Miscanthus Lutarioriparius.

Polyurethane foam is commonly used in the automobile industry due to its favorable acoustic performances. In this study, a new tung oil-based polyurethane composite foam (TOPUF) was prepared by a one-step method. Different forms and contents of miscanthus lutarioriparius (ML) were used in TOPUF for improving acoustic performance. Polyurethane foams were characterized by means of Fourier transform infrared and SEM. The acoustic properties and mechanical properties of TOPUF, obtained with ML, were determined and compared with pure petroleum-based polyurethane foam. The results illustrate that the modification of TOPUF with the ML has a positive effect on the acoustic and mechanical properties in comparison to the unmodified foam. TOPUF obtained with ML powders has better acoustic performance than that obtained with ML strips. The optimum acoustic performance is achieved at the filler content of 0.3 wt%. The average sound absorption coefficient and transmission loss can reach 0.518, and 19.05 dB, respectively.

Multi-Objective Optimization of Acoustic Performances of Polyurethane Foam Composites.

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.