One-Step Fabrication Bioinspired Flexible Hierarchical Micro-Nano Structures with Different Morphologies.

In this study, different flexible structures that are morphologically like the micro-nano pillars, setae, and cilia on many natural organism surfaces are created with a novel fabricating strategy to explore the phenomenon and mechanism of static and dynamic droplets forming on them. Just by adjusting the mold pattern during fabrication, different micro/nanomorphologies including micro-nano pillar, filament, or flake arrays could be conveniently obtained on a pristine smooth film surface. Due to the existence of uniformly distributed hierarchical micro-nano structure arrays that are composed of top-down nanoscale filamentous tips, micro block bases, and grooves on the film, air trapped in arrays connects the atmosphere continuously and forms a successive air-pocket layer, which greatly reduces the solid-liquid interfacial fraction and endows the micro-nanotextured film with the capability of superhydrophobicity, low-adhesion, self-cleaning, anti-icing, and deicing characteristics. Through various mechanical and chemical tests, the film has demonstrated its robustness, making it highly suitable for a wide range of practical engineering applications.

A Novel Type of Boundary Extraction Method and Its Statistical Improvement for Unorganized Point Clouds Based on Concurrent Delaunay Triangular Meshes.

Currently, three-dimensional (3D) laser-scanned point clouds have been broadly applied in many important fields, such as non-contact measurements and reverse engineering. However, it is a huge challenge to efficiently and precisely extract the boundary features of unorganized point cloud data with strong randomness and distinct uncertainty. Therefore, a novel type of boundary extraction method will be developed based on concurrent Delaunay triangular meshes (CDTMs), which adds the vertex-angles of all CDTMs around a common data point together as an evaluation index to judge whether this targeted point will appear at boundary regions. Based on the statistical analyses on the CDTM numbers of every data point, another new type of CDTM-based boundary extraction method will be further improved by filtering out most of potential non-edge points in advance. Then these two CDTM-based methods and popular α-shape method will be employed in conducting boundary extractions on several point cloud datasets for comparatively analyzing and discussing their extraction accuracies and time consumptions in detail. Finally, all obtained results can strongly demonstrate that both these two CDTM-based methods present superior accuracies and strong robustness in extracting the boundary features of various unorganized point clouds, but the statistically improved version can greatly reduce time consumption.

Adaptive variational mode decomposition and its application to multi-fault detection using mechanical vibration signals.

Vibration-based feature extraction of multiple transient fault signals is a challenge in the field of rotating machinery fault diagnosis. Variational mode decomposition (VMD) has great potential for multiple faults decoupling because of its equivalent filtering characteristics. However, the two key hyper-parameters of VMD, i.e., the number of modes and balancing parameter, require to be predefined, thereby resulting in sub-optimal decomposition performance. Although some studies focused on the adaptive parameter determination, the problems in these improved methods like mode redundancy or being sensitive to random impacts still need to be solved. To overcome these drawbacks, an adaptive variational mode decomposition (AVMD) method is developed in this paper. In the proposed method, a novel index called syncretic impact index (SII) is firstly introduced for better evaluation of the complex impulsive fault components of signals. It can exclude the effects of interference terms and concentrate on the fault impacts effectively. The optimal parameters of VMD are selected based on the index SII through the artificial bee colony (ABC) algorithm. The envelope power spectrum, proved to be more capable for fault feature extraction than the envelope spectrum, is applied in this study. Analysis on simulated signals and two experimental applications based on the proposed method demonstrates its effectiveness over other existing methods. The results indicate that the proposed method outperforms in separating impulsive multi-fault signals, thus being an efficient method for multi-fault diagnosis of rotating machines.