Flexure hinges, which serve as the crucial joints in a large number of compliant mechanisms, have been widely applied in a variety of significant fields where there is high demand for the micro/nano motions with high resolution and high precision. Currently, an increasing number of notched flexure hinges with different structures and performances have been rapidly developed, but the existing performance comparisons on different notched flexure hinges were only conducted on seldom typical structures and are far from the comprehensiveness and fairness due to the different comparative conditions and discrepant evaluating indexes. Therefore, the finite beam-based matrix modeling method and nondimension precision factors will be employed in comprehensive comparing and ranking of 13 types of frequently-used notched flexure hinges in terms of their main compliances, motion accuracies, and stress concentrations, further providing useful practical guidelines to develop the compliant mechanisms with excellent overall performances.

The cutting forces will generally suffer massive complex factors, such as material deformation, tool eccentricity and system vibration, which will inevitably induce many great difficulties in accurately modeling the cutting force predictions that are very significant to investigate cutting processes. Therefore, the genetic algorithm optimized back-propagation and particle swarm optimization neural networks will be adopted to effectively construct cutting force prediction models. In these two back-propagation prediction models, the main milling parameters will be defined into their input vectors, and the transient milling forces along three different directions will be selected as their output vectors, then the implicit relationships between input and output vectors can be directly generated through practically training and learning these two built back-propagation models with a set of experimental milling force data. Meanwhile, the finite element analysis method will be also used to predict milling forces through programming two easy-to-operate plug-ins that can efficiently construct finite element analysis models, conveniently define processing parameters, and automatically perform mesh generation. Subsequently, the milling forces predicted by the established genetic algorithm optimized back-propagation and particle swarm optimization back-propagation models will be analytically compared with finite element analysis simulations and experiments; also the stress distribution and chip formations of finite element analysis and experiments will be comparatively investigated. Finally, the obtained results clearly indicate that these two back-propagation models built by artificial neural networks can well agree with finite element analysis simulations and experiments, but the particle swarm optimization back-propagation model is superior to the genetic algorithm optimized back-propagation model, which clearly demonstrate the particle swarm optimization back-propagation model has higher efficiencies and accuracies in predicting the average and transient cutting forces for different milling processes on aluminum alloy 7050.

The flapping wing micro air vehicle (FWMAV) has been attracting lots of interest since the 1990s and is one of the research hotspots in microminiaturization design. However, along with the miniaturization of FWMAV development, flight endurance becomes the bottleneck that significantly impedes the rapid development for these aircrafts because of the critical limit in energy supply due to the limited overall size and weight. In this paper, energy recovery technology was developed for FWMAV with the new type polyvinylidene fluoride (PVDF) piezoelectric wing which could generate the electric potential energy caused by the wing deformation due to the characteristics of the PVDF material. A single crank double rocker mechanism flapping platform was designed to test the deformation energy collection effect and aerodynamic lift. The PVDF wing surface was divided into 16 grid areas to be measured respectively. The lift, output voltage and output power variations for the different flapping frequency was successfully obtained in tests. By analyzing test data, if could be found that the output power could reflect the flutter condition without equipping other sensors and adding extra weight to the aircraft. Moreover, when the flapping frequency was accelerated to 12 Hz, the output power and root mean square (RMS) voltage could increase to 21 μW and 6 V respectively, which is enough to power micro electronic devices such as LED lights.

The magnetorheological jet polishing (MJP) technique, as a revolutionary flexible contact polishing approach, is exceptionally suitable for the smooth and ultra-smooth machining of functional surfaces with tiny or microstructures due to the following essential advantages. Machine tool accuracy is not required and there is nearly no tool wear in addition to high polishing efficiency, minimal surface damage, great surface shape adaptation, superior material removal process selectivity, and so on. This work examines the machining mechanism, the development of machining devices, and the optimization of the process parameters in MJP. This review also covers the MJP technique’s existing limitations and opportunities.

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.