Addressing the challenges of bulky, low-efficiency sound-insulation materials at low frequencies, this work proposes an acoustic metamaterial based on curve fractal channels. Each unit cell comprises a concentric circular-ring channel recursively iterated: as the fractal order increases, the channel path length grows exponentially, enabling outstanding sound-insulation performance within a deep-subwavelength thickness. Finite-element and transfer-matrix analyses show that increasing the fractal order from one to three raises the number of bandgaps from three to five and expands total stop-band coverage from 17% to over 40% within a deep-subwavelength thickness. Four-microphone impedance-tube measurements on the third-order sample validate a peak transmission loss of 75 dB at 495 Hz, in excellent agreement with simulations. Compared to conventional zigzag and Hilbert-maze designs, this curve fractal architecture delivers enhanced low-frequency broadband insulation, structural lightweighting, and ease of fabrication, making it a promising solution for noise control in machine rooms, ducting systems, and traffic environments. The method proposed in this paper can be applied to noise reduction of transmission parts for ceramic automation production.

The fault diagnosis of rolling bearing acoustic radiation signals holds significant importance in industrial equipment maintenance. It effectively prevents equipment failures and downtime, ensuring the smooth operation of the production process. Compared with traditional vibration signals, acoustic radiation signals have the advantage of non-contact measurement. They can diagnose faults in special conditions where sensors cannot be installed and provide more comprehensive equipment status information. Therefore, to extract the fault characteristic information of rolling bearings from complex acoustic signals, this paper proposes an advanced deep learning model combining Gramian Angular Field (GAF), ResNet1D, ResNet2D, and multi-head attention mechanism, named CRAMNet (Combined ResNet Attention Multi-Head Net), to diagnose the faults of rolling bearing acoustic radiation signals. Firstly, this method includes converting one-dimensional signals into GAF images and performing data standardization and segmentation. Then, the method utilizes ResNet1D to extract features from one-dimensional signals and ResNet2D to extract features from GAF images. Further, it combines the multi-head attention mechanism to enhance feature representation and capture dependencies between different channels. Finally, this paper compares the proposed method with several traditional models (including CNN, LSTM, DenseNet, and CNN-Transformers). Experimental results show that the proposed method performs outstandingly in terms of accuracy and robustness. The combination of residual networks and multi-head attention mechanism in the model significantly enhances its ability to accurately diagnose rolling bearing faults, proving the superiority of the algorithm.

Tool micro-defect quantification is often degraded by optical sampling limits, boundary aliasing, and non-Gaussian industrial noise. To improve segmentation fidelity and physical measurement reliability, this study proposes a physically guided reconstruction and metrology framework for milling tool defects. The framework first uses an improved ResNet18-BiFPN segmentation network supervised by a distance transform-based boundary-aware loss, which encourages mask boundaries to fit high-curvature crack and chipping regions. A tensor-guided elliptical adaptive structuring element is then introduced to restore local topology while preserving tangential connectivity and normal boundary fidelity. Finally, a stable edge region search and curvature-adaptive gray/Zernike moment fusion strategy are used for noise-robust sub-pixel localization and physical area quantification. Image-based evaluation and controlled simulation-based stress tests show that the proposed method achieves an mIoU of 92.5%, an F1-score of 97.1%, and an HD95 of 2.15 pixels. Under strong synthetic noise, the average distance error remains below 0.28 pixels. For micro-defect area measurement, the mean relative error is reduced to approximately 1.7%. These results indicate that the proposed framework can support more reliable defect metrology for tool condition monitoring under complex industrial imaging conditions.

This paper investigates the contact stress induced by a rigid sphere sliding on a coating-ceramic system. A discrete element model incorporating a ceramic substrate, a surface coating, and a rigid sphere is developed. The influences of the coating grain elastic modulus, coating surface friction coefficient, coating thickness, and interface microcrack defects on the stress distribution within the system are analyzed. The results indicate that a higher coating-to-substrate elastic modulus ratio increases the overall stress but reduces the interfacial shear stress. A lower surface friction coefficient is more beneficial for hard coatings. The relatively optimal coating thickness (h/a) is approximately 0.5. When interface microcrack defects are present, stress concentrations occur at their locations. Longer interface microcracks lead to greater stress concentration, and the interfacial concentrated stress increases with crack length.