In light of ignoring the effect of backlash on mesh stiffness in existing gear dynamic theory, a precise profile equation was established based on the generating processing principle. An improved potential energy method was proposed to calculate the mesh stiffness. The calculation result showed that when compared with the case of ignoring backlash, the mesh stiffness with backlash had an obvious decrease in a mesh cycle and the rate of decline had a trend of decreasing first and then increasing, so a stiffness coefficient was introduced to observe the effect of backlash. The Fourier series expansion was employed to fit the mesh stiffness rather than time-varying mesh stiffness, and the stiffness coefficient was fitted with the same method. The time-varying mesh stiffness was presented in terms of the piecewise function. The single degree of freedom model was employed, and the fourth order Runge–Kutta method was utilized to investigate the effect of backlash on the nonlinear dynamic characteristics with reference to the time history chart, phase diagram, Poincare map, and Fast Fourier Transformation (FFT) spectrogram. The numerical results revealed that the gear system primarily performs a non-harmonic-single-periodic motion. The partially enlarged views indicate that the system also exhibits small-amplitude and low-frequency motion. For different cases of backlash, the low-frequency motion sometimes shows excellent periodicity and stability and sometimes shows chaos. It is of practical guiding significance to know the mechanisms of some unusual noises as well as the design and manufacture of gear backlash.

In the current study, molecular dynamics (MD) simulations were performed to study the pressure dependence of the structural and mechanical properties of single-crystal tungsten. The results show that single-crystal tungsten possesses noteworthy high-pressure stability and exhibits linear lattice contraction with increasing external pressure. Consistent with the results of the performed experiments, the predicted elastic moduli, including Young’s modulus, shear modulus, and bulk modulus, as well as Poisson’s ratio and Pugh’s modulus ratio, show a clear increasing trend with the increase in pressure. Under uniaxial tensile loading, the single-crystal tungsten at high pressures experiences a phase transition from BCC to FCC and other disordered structures, which results in a stripe-like morphology in the tungsten crystal. These results are expected to deepen our understanding of the high-pressure structural and mechanical behaviors of tungsten materials.

Purpose: Effective rail surface defects detection method is the basic guarantee to manufacture high-quality rail. However, the existed visual inspection methods have disadvantages such as poor ability to locate the rail surface region and high sensitivity to uneven reflection. This study aims to propose a bionic rail surface defect detection method to obtain the high detection accuracy of rail surface defects under uneven reflection environments. Design/methodology/approach: Through this bionic rail surface defect detection algorithm, the positioning and correction of the rail surface region can be computed from maximum run-length smearing (MRLS) and background difference. A saliency image can be generated to simulate the human visual system through some features including local grayscale, local contrast and edge corner effect. Finally, the meanshift algorithm and adaptive threshold are developed to cluster and segment the saliency image. Findings: On the constructed rail defect data set, the bionic rail surface defect detection algorithm shows good recognition ability on the surface defects of the rail. Pixel- and defect-level index in the experimental results demonstrate that the detection algorithm is better than three advanced rail defect detection algorithms and five saliency models. Originality/value: The bionic rail surface defect detection algorithm in the production process is proposed. Particularly, a method based on MRLS is introduced to extract the rail surface region and a multifeature saliency fusion model is presented to identify rail surface defects.

The sliding freestanding layer triboelectric nanogenerator (SF-TENG) is a sustainable power source that can convert mechanical energy from linear or rotating mechanical motion to electrical energy. This paper proposes a double-layer staggered chain teeth TENG. Comparing the staggered electrode TENG and the double-layer staggered electrode TENG, the output voltage difference is relatively small. The electrode of the TENG is designed to the shape of chain teeth, which proves that TENG can be combined with a zipper, and the best distance among chain teeth in the TENG is determined through experiments. Compared with traditional zippers, the double-layer staggered chain teeth TENG can generate electrical energy during the continuous pulling of the zipper. The double-layer staggered chain teeth TENG has good performance. When the external load is 20 MΩ, the maximum output power reaches 20.18 µW. After the rectification and transformation, the generated electricity can light up 30 LED lights or more, and can also supply power to electronic devices. Through the chain teeth array, the open circuit voltage and transfer charge generated by the zipper during the continuous pulling process are improved. The double-layer staggered chain teeth TENG has a good usage environment in life, and this work will provide valuable insights for the development of SF-TENG technology.