Picosecond laser trimming of ceramic cores with porous multi-scale particle microstructure

The ceramic core, produced by hot injection molding, is one of the critical components for manufacturing high-performance aircraft engine turbine blades. However, the injection molding process will cause defects such as burrs and flashes in the fine structure of the formed ceramic core. Manual trimming is necessary, but the trimming quality is poor, and the yield is low. In this paper, the online trimming method of ceramic cores is studied. Based on the orthogonal experiment method, the optimal laser parameters for processing the ceramic core's porous multi-scale particle structure material were obtained. Further, the problems of the match head and tail phenomenon and dimensional accuracy improvement in trimming ceramic cores have been studied. A path optimisation method is proposed to improve the quality and accuracy of the trimming profile effectively. Finally, the overall process flow of ceramic core trimming is elaborated, and experimental verification is given. The results show that the ceramic core online trimming method proposed in this paper has advantages of high precision and high yield compared with the manual method, which will have substantial potential application value in the aviation field.     

Investigations on grinding process of woven ceramic matrix composite based on reinforced fiber orientations

Fiber orientations play the decisive role in grinding process of woven ceramic matrix composites, but the influence of woven fibers in grinding process is not clear. This paper studies the surface quality and grinding force by comparing different woven surfaces. Through a series of experiments in optimized sampling conditions, we analyze characteristics of the material surface topography height, wave distribution and surface support properties in details. And we find some outstanding characteristics of the surface microstructure. We also study the influence of grinding processing parameters on surface microstructure. The results show that machining surface which contains more parallel fibers is rougher and more keenness than gauss surface. Grinding wheel speed and depth of cut have great influence on surface topography and surface support properties. And it is discovered that grinding forces are also highly dependent on fiber orientations. The mechanism of the grinding phenomena is also analyzed in this paper according to knowledge of fracture mechanics and mechanical damage phenomenology. The research obtained will be an important technical support on improving the processing quality of woven ceramic matrix composites.     

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

We present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.     

The influence of plastic properties on chip formation

A two-dimensional finite-element model of the chip formation process is used to study the influence of the material law determining plastic flow on chip formation of titanium alloys at high cutting speeds. For simplicity, friction and thermo-mechanical properties of the tool are neglected in the simulations. Titanium chips are strongly segmented and therefore especially suitable to study the role of segmentation for the cutting force and other cutting parameters. Of special interest is the influence of the thermal softening parameter in the material law. Increasing thermal softening leads to a drastic decrease in the cutting force and a corresponding increase in chip segmentation. A variation of the hardening exponent is also performed. It is shown that the simulation results can be understood by using adiabatic flow stress curves. The variation of the hardening exponent leads to strongly differing chip shapes, although the cutting force stays nearly constant. This shows that mean cutting forces should not be used as simulation verification parameters.     

Investigation into polishing process of CVD diamond films

A new technique used for polishing chemical vapor deposition (CVD) diamond films has been investigated, by which rough polishing of the CVD diamond films can be achieved efficiently. A CVD diamond film is coated with a thin layer of electrically conductive material in advance, and then electro-discharge machining (EDM) is used to machine the coated surface. As a result, peaks on the surface of the diamond film are removed rapidly. During machining, graphitization of diamond enables the EDM process to continue. The single pulse discharge shows that the material of the coated layer evidently affects removal behavior of the CVD diamond films. Compared with the machining of ordinary metal materials, the process of EDM CVD diamond films possesses a quite different characteristic. The removal mechanism of the CVD diamond films is discussed.     

北京谱仪漂移室读出电子学校准电路

本文介绍的电路可同时进行时间和电荷校准,它的校准量程大,精度高,稳定性好,可输出864路指数形校准信号,并可手动或程控。它的时间校准精度优于±250ps,电荷量校准精度优于±1％。     

β21s高强度钛合金的铣削试验研究

针对一种新型的难加工钛合金材料β21s,进行了刀具磨损试验和铣削力的测量试验,对材料的切削加工性进行评价,并且给出优选结果,为实际生产提供参考。     

Introduction of a nonlinearity measure for principal component models

Although principal component analysis (PCA) is an important tool in standard multivariate data analysis, little interest has been devoted to assessing whether the underlying relationship within a given variable set can be described by a linear PCA model or whether nonlinear PCA must be utilized. This paper addresses this deficiency by introducing a nonlinearity measure for principal component models. The measure is based on the following two principles: (i) the range of recorded process operation is divided into smaller regions; and (ii) accuracy bounds are determined for the sum of the discarded eigenvalues. If this sum is within the accuracy bounds for each region, the process is assumed to be linear and vice versa. This procedure is automated through the use of cross-validation. Finally, the paper shows the utility of the new nonlinearity measure using two simulation studies and with data from an industrial melter process.     

Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests

The goal of this research was to compare narrowband hyperspectral Hyperion data with broadband hyperspatial IKONOS data and advanced multispectral Advanced Land Imager (ALI) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data through modeling and classifying complex rainforest vegetation. For this purpose, Hyperion, ALI, IKONOS, and ETM+ data were acquired for southern Cameroon, a region considered to be a representative area for tropical moist evergreen and semi-deciduous forests. Field data, collected in near-real time to coincide with satellite sensor overpass, were used to (1) quantify and model the biomass of tree, shrub, and weed species; and (2) characterize forest land use/land cover (LULC) classes.The study established that even the most advanced broadband sensors (i.e., ETM+, IKONOS, and ALI) had serious limitations in modeling biomass and in classifying forest LULC classes. The broadband models explained only 13-60% of the variability in biomass across primary forests, secondary forests, and fallows. The overall accuracies were between 42% and 51% for classifying nine complex rainforest LULC classes using the broadband data of these sensors. Within individual vegetation types (e.g., primary or secondary forest), the overall accuracies increased slightly, but followed a similar trend. Among the broadband sensors, ALI sensor performed better than the IKONOS and ETM+ sensors.When compared to the three broadband sensors, Hyperion narrowband data produced (1) models that explained 36-83% more of the variability in rainforest biomass, and (2) LULC classifications with 45-52% higher overall accuracies. Twenty-three Hyperion narrowbands that were most sensitive in modeling forest biomass and in classifying forest LULC classes were identified and discussed.