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In order to study the influence of yarn bundle vibration characteristics on the vibration and noise of tufted carpet looms, a yarn bundle vibration model was proposed in this paper, which was based on the viscoelasticity of the yarn bundle, and the correctness of the transverse vibration equation of the yarn bundle was verified by experiments. Different creep models of the yarn bundle were fitted with the experimental data, and the transverse vibration equation of the axial motion viscoelastic yarn bundle was established by using Burgers four-element constitutive model. Then, the Galerkin truncation method was used to solve the partial differential vibration equation of the yarn bundle and solve the equation. Finally, the correctness of the vibration equation is verified by comparison between the experimental results and the numerical simulation results. The results show that the vibration equation is suitable for studying the transverse dynamic vibration characteristics of the yarn bundle.

In order to recognise the noise source of a warp knitting machine, a method based on Modified Ensemble Empirical Mode Decomposition (MEEMD) and Akaike Information Criterion (AIC) is proposed. The MEEMD_AIC method is applied to measure the noise signal of a warp knitting machine and analyse every single effective component selected. Noise source identification is realised by combining the vibration signal characteristics of the main parts of the warp knitting machine. Firstly, MEEMD is used to decompose the measured noise signal of the warp knitting machine into a finite number of intrinsic mode function (IMF) components. Then, singular value decomposition (SVD) is performed on the covariance matrix of the component matrix to get the eigen value of the matrix. Next, the number of effective components is estimated based on the AIC criterion, and the effective components are selected by combining the energy characteristic index and the Pearson correlation coefficient method. The results show that the noise signal of the warp knitting machine is a mixture of multiple noise source signals. The main noise sources of the warp knitting machine, including the vibration of the pulling roller, the main shaft of the loop forming mechanism and the push rod of the guide bar traverse the mechanism, provide theoretical support for recognition of the active noise reduction of the warp knitting machine using the MEEMD_AIC method.

In recent years, the noise reduction research of the carpet tufting machine has been developing slowly. The research gaps of the existing work mainly focus on the noise source identification for the carpet tufting machine. MEEMD (EEMD) has been proposed to apply to source recognition on textile machinery. Due to the uniqueness of the MEEMD/EEMD, it is difficult to set suitable white noise control parameters. MEEMD (EEMD) has only been tested via simulation; however, it has not been mathematically proven or evaluated. This leads to inevitable flaws in the research conclusions, and even some conclusions are wrong. The contribution of this paper is twofold. First, in order to recognize the noise source of a carpet tufting machine, a method based on complete ensemble empirical mode decomposition (CEEMDAN) and Akaike information criterion (AIC) is proposed. The CEEMDAN-AIC method is applied to measure the noise signal of a carpet tufting machine and analyzed every single effective component selected. Noise source identification is realized by combining the vibration signal characteristics of the main parts of the carpet tufting machine. CEEMDAN is used to decompose the measured noise signal of the carpet tufting machine into a finite number of intrinsic mode functions (IMFs). Then, singular value decomposition (SVD) is performed on the covariance matrix of the IMF matrix to obtain the eigenvalue. Next, the number of effective IMFs is estimated based on the AIC criterion, and the effective IMFs are selected by combining the energy characteristic index and the Pearson correlation coefficient method. Furthermore, reconstruction and comparison of the decomposed signals of MEEMD and CEEMDAN proved that CEEMDAN is effective and accurate in source recognition. The results show that the noise signal of the carpet tufting machine is a mixture of multiple noise source signals. The main noise sources of the carpet tufting machine include shock caused by the impact of the tufted needle and looped hook and vibration of the hook-driven shaft and pressure plate. It provides theoretical support for the noise reduction of the carpet tufting machine.

To investigate the mechanical properties of embedded honeycomb plates with high efficiency and accuracy, a new multilayered equivalent finite element method (FEM) model is proposed. A series of FEM numerical studies (modal analysis, static analysis, and shock spectrum analysis) are performed. The goal is to compare the errors produced by the multilayered equivalent method and by existing equivalent approaches. The obtained results indicate that the proposed model shows good agreement with the original plate. Moreover, based on the new model, a parametric study correlating the microstructure parameters (embedded depth/cell size) to modal frequency is proposed, and a multiparameter equation for frequency and embedded depth/cell size is established to serve as a basis for structural optimization design.

In a typical carpet tufting machine, kinematic and dynamic characteristics of the needle multi-linkage mechanism are the important factors affecting the quality of the tufting carpet. For providing a rational basis for mechanism design and vibration characteristic analysis, a mathematical model of the needle multi-linkage mechanism is constructed using the complex vector analysis method. On the basis of the model, kinematic characteristic curves and dynamic characteristic curves of the needle multi-linkage mechanism are analysed by simulation methods. Finally experimental validation of the alternating load dynamic characteristics is performed on the needle multi-linkage mechanism in a typical carpet tufting machine. The results prove the theoretical analysis validity of the needle multi-linkage mechanism.

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