Highly stretchable and robust superhydrophobic surfaces have attracted tremendous interest due to their broad application prospects. In this work, silicone elastomers were chosen to fabricate superhydrophobic surfaces with femtosecond laser texturing method, and high stretchability and tunable adhesion of the superhydrophobic surfaces were demonstrated successfully. To our best knowledge, it is the first time flexible superhydrophobic surfaces with a bearable strain up to 400% are fabricated by simple laser ablation. The test also shows that the strain brings no decline of water repellency but an enhancement to the superhydrophobic surfaces. In addition, a stretching-induced transition from “petal” state to “lotus” state of the laser-textured surface was also demonstrated by non-loss transportation of liquid droplets. Our results manifest that femtosecond laser ablating silicone elastomer could be a promising way for fabricating superhydrophobic surface with distinct merits of high stretchability, tunable adhesion, robustness, and non-fluorination, which is potentially useful for microfluidics, biomedicine, and liquid repellent skin.

In this paper, a new approach is proposed based on data fusing with vibration signals using time-frequency parameters, probabilistic principal component analysis (PPCA) and statistical inference, for improving the accuracy and visibility of damage identification for numerical control (NC) machine tools. Time-frequency feature principal components are put forward, which extracted from eight dimensionless parameters statistically in the time and frequency domains by PPCA. The Chi-2 statistic is established according to statistical inference principle, and the feature figure of principal components is built that can acquire damage distribution of tools by measured data. An empirical analysis in NC milling machine tools is developed, and the result shows high accuracy and visibility of the proposed approach.

Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. However, current methods of tool fault diagnosis lack accuracy. Therefore, in the present paper, a fault diagnosis method was proposed based on stationary subspace analysis (SSA) and least squares support vector machine (LS-SVM) using only a single sensor. First, SSA was used to extract stationary and non-stationary sources from multi-dimensional signals without the need for independency and without prior information of the source signals, after the dimensionality of the vibration signal observed by a single sensor was expanded by phase space reconstruction technique. Subsequently, 10 dimensionless parameters in the time-frequency domain for non-stationary sources were calculated to generate samples to train the LS-SVM. Finally, the measured vibration signals from tools of an unknown state and their non-stationary sources were separated by SSA to serve as test samples for the trained SVM. The experimental validation demonstrated that the proposed method has better diagnosis accuracy than three previous methods based on LS-SVM alone, Principal component analysis and LS-SVM or on SSA and Linear discriminant analysis.

In this study, laser sintering of TiO₂ nanoparticle films on plastic substrates was conducted in order to improve the incident photon-to-electron conversion efficiency (IPCE) of flexible dye-sensitized solar cells (DSCs). Lasers with different wavelengths (355 nm and 1064 nm) were used to process the TiO₂ electrodes. With an optimized processing parameter combination, the 1064 nm laser can sinter 13 μm thick TiO₂ films uniformly, but the uniform sintering cannot be achieved by the 355nm ultraviolet (UV) laser, since the films possess a high absorption ratio at 355 nm. The experimental results demonstrate that the near-infrared laser sintering can enhance the electrical connection between TiO₂ nanoparticles without destroying the flexible plastic substrate, reduce the transmission impedance of electrons and increase the absorption rate of incident light. Furthermore, the charge collection efficiency, fill factor, and short-circuit current have all been improved to some extent, and the solar conversion efficiency increased from 4.6% to 5.7%, with an efficiency enhancement reaching 23.9%.

The hybrid machining method called electrochemical mill-grinding is an advanced machining technology, which can achieve high-quality processing of various difficult-to-cut materials. However, achieving rapid transportation and removal of processed products within a small machining gap is a key challenge, which directly affects the final machined surface quality. In this paper, the product transport under different flushing modes was studied through numerical simulation of the flow field. By using the particle tracking method, the time required for the removal of machining products from the machining gap was dynamically simulated. The analysis results indicated that the removal speed of machining products could be significantly improved when the flushing pressure was 0.8 MPa and the electrolyte jet angle was 30°. In addition, machining experiments were conducted. The machining experiment results showed that the machined surface had a metallic luster with clear edge contours. The grinding marks on the machined surface indicated the grinding effect. The continuous machining of complex patterns demonstrated the reliability of this hybrid machining process.