Chaozhi Cai, Yuqi Ren, Yingfang Xue^*, Jianhua Ren

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 665-682, 2025, DOI:10.32604/sdhm.2025.059044 - 03 April 2025

Abstract Rolling bearings are important parts of industrial equipment, and their fault diagnosis is crucial to maintaining these equipment’s regular operations. With the goal of improving the fault diagnosis accuracy of rolling bearings under complex working conditions and noise, this study proposes a multiscale information fusion method for fault diagnosis of rolling bearings based on fast Fourier transform (FFT) and variational mode decomposition (VMD), as well as the Senet (SE)-TCNnet (TCN) model. FFT is used to transform the original one-dimensional time domain vibration signal into a frequency domain signal, while VMD is used to decompose the… More >

Qingxiang Pei^1,2, Fan Li^2,3, Ziheng Fei⁴, Haojie Lian^2,3, Xiaohui Yuan^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 79-96, 2025, DOI:10.32604/cmc.2025.061741 - 26 March 2025

Abstract This paper employs the Direct Finite Element Squared (DFE²) method to develop Sparse Polynomial Chaos Expansions (SPCE) models for analyzing the electromechanical properties of multiscale piezoelectric structures. By incorporating variations in piezoelectric and elastic constants, the DFE² method is utilized to simulate the statistical characteristics—such as expected values and standard deviations—of electromechanical properties, including Mises stress, maximum in-plane principal strain, electric potential gradient, and electric potential, under varying parameters. This approach achieves a balance between computational efficiency and accuracy. Different SPCE models are used to investigate the influence of piezoelectric and elastic constants on multiscale piezoelectric More >

Xin Wen^*, Xiao Zheng, Yu He

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4371-4388, 2025, DOI:10.32604/cmc.2025.060661 - 06 March 2025

Abstract Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation. However, existing detection methods often struggle with challenges such as complex defect morphology, texture similarity, and fuzzy edges, leading to poor accuracy and missed detections. In order to resolve these problems, we propose MSCM-Net (Multi-Scale Cross-Modal Network), a multiscale cross-modal framework focused on detecting rail surface defects. MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps, effectively capturing and enhancing features at different scales for each modality. To… More >

Xiaolin Li¹, Xinyue Li¹, Liming Zhou^2,*, Hangran Yang¹, Xiaoqing Yuan¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3821-3841, 2025, DOI:10.32604/cmc.2025.059937 - 06 March 2025

Abstract Magneto-electro-elastic (MEE) materials are widely utilized across various fields due to their multi-field coupling effects. Consequently, investigating the coupling behavior of MEE composite materials is of significant importance. The traditional finite element method (FEM) remains one of the primary approaches for addressing such issues. However, the application of FEM typically necessitates the use of a fine finite element mesh to accurately capture the heterogeneous properties of the materials and meet the required computational precision, which inevitably leads to a reduction in computational efficiency. To enhance the computational accuracy and efficiency of the FEM for heterogeneous… More >

Shanqiao Huang¹, Zifeng Yuan^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2893-2924, 2025, DOI:10.32604/cmes.2025.059948 - 03 March 2025

Abstract The multiscale computational method with asymptotic analysis and reduced-order homogenization (ROH) gives a practical numerical solution for engineering problems, especially composite materials. Under the ROH framework, a partition-based unitcell structure at the mesoscale is utilized to give a mechanical state at the macro-scale quadrature point with pre-evaluated influence functions. In the past, the “1-phase, 1-partition” rule was usually adopted in numerical analysis, where one constituent phase at the mesoscale formed one partition. The numerical cost then is significantly reduced by introducing an assumption that the mechanical responses are the same all the time at the… More >

Hongxing Wang¹, Xilai Ju², Hua Zhu^1,*, Huafeng Li^1,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1417-1437, 2025, DOI:10.32604/cmc.2024.058785 - 03 January 2025

Abstract Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals, which has certain limitations. Conversely, deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency. Recently, utilizing the respective advantages of convolution neural network (CNN) and Transformer in local and global feature extraction, research on cooperating the two have demonstrated promise in the field of fault diagnosis. However, the cross-channel convolution mechanism in CNN and the self-attention calculations in… More > Graphic Abstract

Xiaoyun Chen¹, Lanyao Zhang¹, Xiaoling Chen¹, Yigang Cen², Linna Zhang^1,*, Fugui Zhang¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 521-542, 2025, DOI:10.32604/cmc.2024.058063 - 03 January 2025

Abstract Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules. In the production process, defect samples occur infrequently and exhibit random shapes and sizes, which makes it challenging to collect defective samples. Additionally, the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions. This paper proposes a novel Lightweight Multi-scale Feature Fusion network (LMFF) to address these challenges. The network comprises a feature extraction network, a multi-scale feature fusion module (MFF), and a segmentation network. Specifically, a feature extraction network is proposed to obtain… More >

Jing Wu¹, E Zhou¹, An Huang¹, Hongbin Zhang², Ming Hu³, Guangzhao Qin^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.012552

Abstract High thermal conductivity substrate plays a significant role for efficient heat dissipation of electronic devices, and it is urgent to optimize the interfacial thermal resistance. As a novel material with ultra-high thermal conductivity second only to diamond, boron arsenide (BAs) shows promising applications in electronics cooling [1,2]. By adopting multi-scale simulation method driven by machine learning potential, we systematically study the thermal transport properties of boron arsenide, and further investigate the interfacial thermal transport in the GaN-BAs heterostructures. Ultrahigh interfacial thermal conductance of 260 MW m^-2K^-1 is achieved, which agrees well with experimental measurements, and the More >

Yoshitaka Umeno^1,*, Atsushi Kubo², Emi Kawai¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-2, 2024, DOI:10.32604/icces.2024.012708

Abstract Fatigue fracture accounts for a substantial fraction of failure cases in industrial products, especially in metal materials. While the mechanism of fatigue crack propagation can be understood in the mechanical point of view considering the effect of microstructures and crystal orientations on crack growth, there is still much room for investigations of the mechanism of fatigue crack formation under cyclic loading. It is widely understood that the fatigue crack formation in macroscopic metal materials originates in the persistent slip band (PSB) formed as a result of self-organization of dislocation structures [1]. Nevertheless, the PSB formation… More >

Ryan Murphy^1,*, Dilaksan Thillaithevan¹, Matthew Santer¹, Rob Hewson¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.011402

Abstract In this work we describe the multiscale optimization of non-linear structures. This work moves beyond classical multiscale optimization for linear problems to account for large deformations occurring across the scales of the problem. A multiscale approach is adopted based on the homogenization theory which is used to characterize a parameterized representative volume element (RVE). This RVE characterization is undertaken for both changes in the geometry and the strain applied to the RVE. This latter is a key difference between multiscale approaches for non-linear problems and those for linear problems. This is because the characteristics of… More >