Undamaged measurement of the sub-micron diaphragm and gap by tri-beam interference

A simple, high-accuracy and non-destructive method for the measurement of diaphragm thickness and microgap width based on modulated tri-beam interference is demonstrated. With this method, a theoretical estimation error less than 0.5% for a diaphragm thickness of ~1 μm is achievable. Several fiber-tip air bubbles with different diaphragm thicknesses (6.25, 5.0, 2.5 and 1.25 μm) were fabricated to verify our proposed measurement method. Furthermore, an improved technique was introduced by immersing the measured object into a liquid environment to simplify a four-beam interference into tri-beam one. By applying this improved technique, the diaphragm thickness of a fabricated in-fiber rectangular air bubble is measured to be about 1.47 μm, and the averaged microgap width of a standard silica capillary is measured to be about 10.07 μm, giving a corresponding measurement error only 1.27% compared with actual scanning electron microscope (SEM) results

Comparison and Analysis on Mechanical Property and Machinability about Polyetheretherketone and Carbon-Fibers Reinforced Polyetheretherketone

The aim of this paper is to compare the mechanical property and machinability of Polyetheretherketone (PEEK) and 30 wt% carbon-fibers reinforced Polyetheretherketone (PEEK CF 30). The method of nano-indentation is used to investigate the microscopic mechanical property. The evolution of load with displacement, Young’s modulus curves and hardness curves are analyzed. The results illustrate that the load-displacement curves of PEEK present better uniformity, and the variation of Young’s modulus and hardness of PEEK both change smaller at the experimental depth. The machinability between PEEK and PEEK CF 30 are also compared by the method of single-point diamond turning (SPDT), and the peak-to-valley value (PV) and surface roughness (Ra) are obtained to evaluate machinability of the materials after machining. The machining results show that PEEK has smaller PV and Ra, which means PEEK has superior machinability

Finite Element Analysis and Simulation about Microgrinding of SiC

The application of silicon carbide (SiC) is often limited due to its low machining efficiency and unpredictability about the results of the grinding process. The aim of this paper is to set up finite element analysis models (FEM) about microgrinding process of SiC, to study the change processes about tangential and normal grinding force which can lead to stress and strain inside SiC material under different grinding parameters, and to predict the results before the grinding process. Adaptive remeshing technique is used to minimize the computational time without sacrificing the accuracy of the results in the simulation of SiC grinding process. The research results can be used to choose reasonable grinding parameters based on the required surface quality

Analysis on deformation characteristics of surrounding rock of gob-side entry retaining with soft bottom in thick coal seam and strengthening support technology of roof and side

The deformation and failure mechanism of surrounding rock and the control measures under the condition of long-time high superimposed stress are the keys to gob-side entry retaining support technology in thick coal seam with soft bottom. The existing research on the deformation and failure mechanism of surrounding rock and support control of gob-side entry retaining in thick coal seam is mainly aimed at deformation of roof and side of gob-side entry with hard rock bottom, and the strength of filling body and material proportion. There are few research on retaining roadway with soft bottom in thick coal seam. The mechanical analysis of gob-side entry retaining is incomplete, and the support scheme is single. In order to solve the above problems, taking N1303 working face of Gucheng Coal Mine of Shanxi Lu'an Chemical Industry Group Co., Ltd. as the engineering background, the failure mechanics models of roof, coal wall and floor are established. The deformation and failure characteristics of the roadway surrounding rock are analyzed. The roof is in a mixed stress environment, which is prone to tensile failure. Under the action of high stress, the solid coal side suffers compression shear failure, and the anchor rod fails. The filling body intrudes into the floor under pressure, causing the floor to tilt and lose stability, which is prone to soft coal broken and swelling. According to the deformation and failure characteristics of surrounding rock, the trinity surrounding rock support control scheme is proposed, namely, controlling the roof, restricting the coal side and yielding floor. In order to ensure that the roof can balance the stress distribution above the gob-side entry retaining, the method of anchor cable + filling body top cutting is adopted. Thus the roof does not form a cantilever beam structure above the roadway, only sinking occurs, and there is no rotary deformation. Considering the roof stability of gob-side entry retaining, the way of grouting anchor cable is adopted to grout the broken roof of the roadway to form a whole for better controlling the roof. In order to improve the support strength of the solid coal side, short anchor cables are added to connect the coal seam in the limit equilibrium area with the deep elastic bearing layer, and reduce the support resistance of the filling body beside the roadway. The proper yielding of the floor is beneficial to the flexible support of the whole roadway. The floor is reinforced by digging grooves and pouring strip foundations under the filling body wall. The original gob-side entry retaining support scheme is optimized by using the trinity surrounding rock support control scheme. The field test results show that after using the optimized support scheme, the roof movement subsidence is reduced from 337 mm to 142 mm, and the coal side movement is reduced from 305 mm to 70 mm. The floor movement is reduced from 675 mm to 162 mm, and the roadway convergence rate is reduced from 34.1% to 10.73%. The working resistance of the anchor rod (cable) is stable, the filling body is free of damage and inclination, and the support effect is good

Characterization of the complete chloroplast genome of Homalocladium platycladum (Polygonaceae) and its phylogenetic analysis

Homalocladium platycladum is a fascinating ornamental plant that has long been used in Chinese medicine. Here, we characterize the complete chloroplast genome sequence of this plant (GenBank: NC_062330). This circular genome has a total length of 163,202 bp containing a large single-copy region (87,820bp), a small single-copy region (13,538bp), and a pair of inverted repeat regions (30,922bp). A total of 130 predicted genes were identified, including 85 protein-coding genes, 37 transfer RNA genes, and 8 ribosomal RNA genes. Phylogenetic analysis demonstrated that H. platycladum belongs to the Polygonaceae family and is highly analogous with Homalocladium and Muehlenbeckia families

Ultra-Precision Machining of a Compound Sinusoidal Grid Surface Based on Slow Tool Servo

Compound sinusoidal grid surface with nanometric finish plays a significant role in modern systems and precision calibrator, which can make the systems smaller, the system structure more simple, reduce the cost, and promote the performance of the systems, but it is difficult to design and fabricate by traditional methods. In this paper, a compound freeform surface constructed by a paraboloidal base surface and sinusoidal grid feature surface is designed and machined by slow tool servo (STS) assisted with single point diamond turning (SPDT). A novel combination of the constant angle and constant arc-length method is presented to optimize the cutting tool path. The machining error prediction model is analyzed for fabricating the compound sinusoidal grid surface. A compound sinusoidal grid surface with 0.03 mm amplitude and period of 4 is designed and cutting process is simulated by use of MATLAB software, machining experiment is done on ultra-precision machine tool, the surface profile and topography are measured by Taylor Hobson and Keyence VR-3200, respectively. After dealing with the measurement data of compound freeform surface, form accuracy 4.25 μm in Peak Village value (PV), and surface roughness 89 nm in Ra are obtained for the machined surface. From the theoretical analysis and experimental results, it can be seen that the proposed method is a reasonable choice for fabricating the compound sinusoidal grid surface

Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder

Due to the shortage of independent and identically distributed (i.i.d.) training samples, space−time adaptive processing (STAP) often suffers remarkable performance degradation in the heterogeneous clutter environment. Sparse recovery (SR) techniques have been introduced into STAP for the benefit of the drastically reduced training requirement, but they are incompletely robust for involving the tricky selection of hyper−parameters or the undesirable point estimation for parameters. Given this issue, we incorporate the Multiple−measurement Complex−valued Variational relevance vector machines (MCV) to model the space−time echoes and provide a Gibbs−sampling−based method to estimate posterior distributions of parameters accurately. However, the Gibbs sampler require quantities of iterations, as unattractive as traditional Bayesian type SR−STAP algorithms when the real−time processing is desired. To address this problem, we further develop the Bayesian Autoencoding MCV for STAP (BAMCV−STAP), which builds the generative model according to MCV and approximates posterior distributions of parameters with an inference network pre−trained off−line, to realize fast reconstruction of measurements. Experimental results on simulated and measured data demonstrate that BAMCV−STAP can achieve suboptimal clutter suppression in terms of the output signal to interference plus noise ratio (SINR) loss, as well as the attractive real−time processing property in terms of the convergence rate and computational loads

Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder

Due to the shortage of independent and identically distributed (i.i.d.) training samples, space−time adaptive processing (STAP) often suffers remarkable performance degradation in the heterogeneous clutter environment. Sparse recovery (SR) techniques have been introduced into STAP for the benefit of the drastically reduced training requirement, but they are incompletely robust for involving the tricky selection of hyper−parameters or the undesirable point estimation for parameters. Given this issue, we incorporate the Multiple−measurement Complex−valued Variational relevance vector machines (MCV) to model the space−time echoes and provide a Gibbs−sampling−based method to estimate posterior distributions of parameters accurately. However, the Gibbs sampler require quantities of iterations, as unattractive as traditional Bayesian type SR−STAP algorithms when the real−time processing is desired. To address this problem, we further develop the Bayesian Autoencoding MCV for STAP (BAMCV−STAP), which builds the generative model according to MCV and approximates posterior distributions of parameters with an inference network pre−trained off−line, to realize fast reconstruction of measurements. Experimental results on simulated and measured data demonstrate that BAMCV−STAP can achieve suboptimal clutter suppression in terms of the output signal to interference plus noise ratio (SINR) loss, as well as the attractive real−time processing property in terms of the convergence rate and computational loads