微型注模机的研制

新型微型注模机设备适用于物料搅拌混合和试样注模,特点是适合少量聚合物料的填充改性。本文阐述了该装置的工作原理,并与FUZZY—PID相结合的控制算法及在混炼杯和模具恒温控制中的应用。

Experimental study and numerical analysis on bearing behaviors of super-long rock-socketed bored pile groups

A centrifuge modeling test and a three-dimensional finite element analysis（FEA）of super-long rock-socketed bored pile groups of the Tianxingzhou Bridge are proposed.Based on the similarity theory,different prototypical materials are simulated using different indicators in the centrifuge model.The silver sand,the shaft and the pile cap are simulated according to the natural density,the compressive stiffness and the bending stiffness,respectively.The finite element method（FEM）is implemented and analyzed in ANSYS,in which the stress field during the undisturbed soil stage,the boring stage,the concrete-casting stage and the curing stage are discussed in detail.Comparisons in terms of load-settlement,shaft axial force distribution and lateral friction between the numerical results and the test data are carried out to investigate the bearing behaviors of super-long rock-socketed bored pile groups under loading and unloading conditions.Results show that there is a good agreement between the centrifuge modeling tests and the FEM.In addition,the load distribution at the pile top is complicated,which is related to the stiffness of the cap,the corresponding assumptions and the analysis method.The shaft axial force first increases slightly with depth then decreases sharply,and the rate of decrease in rock is greater than that in sand and soil.

BOY2004年将第13次参展K展

今年的“K”展将于10月20日到27日在德国Dusseldorf举行，BOY公司的产品将会占据13个展位，总展示空问共计444平方米。BOY所用的口号是“为优胜者而制的设备”，展出代表性的机床系列颇具吸引力，他们会在公司位于十三号厅的展位展出六台注模机，在合作公司的展位展出7台其他机床。

Rheological property and drying mechanism of thermoresponsive gelcasting of ZnO

To solve the problem of drying gelcast green body, the thermoresponsive gel system which contains macromonomer graft chains was used in gelcasting of ZnO. The effects of the amount and length of graft chains macromonomer PIPAAm, the total amount of organic matters, and the solid loading on the rheological properties of suspensions were investigated, and the drying mechanism of gelcast green body was analyzed. The results show that ZnO suspensions with the gel system still display shear- thinning rheological behavior, but its viscosity increases with increasing the addition amount and relative molecular mass of PIPAAm graft chain, and the total organic matter content. The PIPAAm graft chains inhibit or even eliminate the formation of the ＂dense layer” on the surface of gelcast ZnO green bodies, and accelerate the drying of green bodies. The introduction of PIPAAm graft chains facilitates the shrinkage of the gelcast ZnO green bodies, which is a feasible method to increase the relative density of green bodies.

MobileNet network optimization based on convolutional block attention module

Deep learning technology is widely used in computer vision.Generally,a large amount of data is used to train the model weights in deep learning,so as to obtain a model with higher accuracy.However,massive data and complex model structures require more calculating resources.Since people generally can only carry and use mobile and portable devices in application scenarios,neural networks have limitations in terms of calculating resources,size and power consumption.Therefore,the efficient lightweight model MobileNet is used as the basic network in this study for optimization.First,the accuracy of the MobileNet model is improved by adding methods such as the convolutional block attention module(CBAM)and expansion convolution.Then,the MobileNet model is compressed by using pruning and weight quantization algorithms based on weight size.Afterwards,methods such as Python crawlers and data augmentation are employed to create a garbage classification data set.Based on the above model optimization strategy,the garbage classification mobile terminal application is deployed on mobile phones and raspberry pies,realizing completing the garbage classification task more conveniently.

Nonlinear Modeling of a High Precision Servo Injection Molding Machine Including Novel Molding Approach

A nonlinear mathematical model of the injection molding process for electrohydraulic servo injection molding machine (IMM) is developed.It was found necessary to consider the characteristics of asymmetric cylinder for electrohydraulic servo IMM.The model is based on the dynamics of the machine including servo valve,asymmetric cylinder and screw,and the non-Newtonian flow behavior of polymer melt in injection molding is also considered.The performance of the model was evaluated based on novel approach of molding - injection and compress molding,and the results of simulation and experimental data demonstrate the effectiveness of the model.

Multivariate time series prediction based on AR_CLSTM

Time series is a kind of data widely used in various fields such as electricity forecasting,exchange rate forecasting,and solar power generation forecasting,and therefore time series prediction is of great significance.Recently,the encoder-decoder model combined with long short-term memory(LSTM)is widely used for multivariate time series prediction.However,the encoder can only encode information into fixed-length vectors,hence the performance of the model decreases rapidly as the length of the input sequence or output sequence increases.To solve this problem,we propose a combination model named AR_CLSTM based on the encoder_decoder structure and linear autoregression.The model uses a time step-based attention mechanism to enable the decoder to adaptively select past hidden states and extract useful information,and then uses convolution structure to learn the internal relationship between different dimensions of multivariate time series.In addition,AR_CLSTM combines the traditional linear autoregressive method to learn the linear relationship of the time series,so as to further reduce the error of time series prediction in the encoder_decoder structure and improve the multivariate time series Predictive effect.Experiments show that the AR_CLSTM model performs well in different time series predictions,and its root mean square error,mean square error,and average absolute error all decrease significantly.

RESEARCH ON THE SEROSITY DURING INJECTING CLAY GROUTS INTO THE OVERLYING ROCK STRATA

On the basis of the mechanism study of injecting clay grouts into overlying strata, the clay grouts are researched in greater detail from three aspects. The flowing state of clay grouts in the strata——the pattern of different direction flowing around a point source is advanced and the flowing equation is put forward which is correspond with experiment result, and the corresponding mechanical model is set up which has its formulistic study, and the function of clay grouts is also discussed after the water in it has been lost, at the same time the concept of similar rock in effective supporting zone is given. It would draw great positive inspiration from what studied in this paper for studying on drawing down the surface subsidence by injecting.

B-SPLINE-BASED SVM MODEL AND ITS APPLICATIONS TO OIL WATER-FLOODED STATUS IDENTIFICATION

A method of B-spline transform for signal feature extraction is developed. With the B-spline, the log-signal space is mapped into the vector space. An efficient algorithm based on Support Vector Machine (SVM ) to automatically identify the water-flooded status of oil-saturated stratum is described. The experiments show that this algorithm can improve the performances for the identification and the generalization in the case of a limited set of samples.

Adaptive multi-modal feature fusion for far and hard object detection

In order to solve difficult detection of far and hard objects due to the sparseness and insufficient semantic information of LiDAR point cloud,a 3D object detection network with multi-modal data adaptive fusion is proposed,which makes use of multi-neighborhood information of voxel and image information.Firstly,design an improved ResNet that maintains the structure information of far and hard objects in low-resolution feature maps,which is more suitable for detection task.Meanwhile,semantema of each image feature map is enhanced by semantic information from all subsequent feature maps.Secondly,extract multi-neighborhood context information with different receptive field sizes to make up for the defect of sparseness of point cloud which improves the ability of voxel features to represent the spatial structure and semantic information of objects.Finally,propose a multi-modal feature adaptive fusion strategy which uses learnable weights to express the contribution of different modal features to the detection task,and voxel attention further enhances the fused feature expression of effective target objects.The experimental results on the KITTI benchmark show that this method outperforms VoxelNet with remarkable margins,i.e.increasing the AP by 8.78%and 5.49%on medium and hard difficulty levels.Meanwhile,our method achieves greater detection performance compared with many mainstream multi-modal methods,i.e.outperforming the AP by 1%compared with that of MVX-Net on medium and hard difficulty levels.

Simulation of stray grain formation during unidirectional solidification of IN738LC superalloy

The influence of casting parameters on stray grain formation of a unidirectionally solidified superalloy IN738LC casting with three platforms was investigated by using a 3D cellular automaton-finite element （CAFE） model in CALCOSOFT package. The model was first validated by comparison of the reported grain structure of AI-7%Si （mass fraction） alloy. Then, the influence of pouring temperature, heat flux of the lateral surface, convection heat coefficient of the cooled chill and mean undercooling of the bulk nucleation on the stray grain formation was studied during the unidirectional solidification. The predictions show that the stray grain formation is obviously sensitive to the pouring temperature, heat flux and mean undercooling of the bulk nucleation. However, increasing the heat convection coefficient has little influence on the stray grain formation.

Double Charm Decays of B Mesons in mSUGRA Model

Based on the low energy effective Hamiltonian with naive factorization, we calculate the branching ratios （BRs） and CP asymmetries （CPAs） for the twenty three double charm decays B/B8 → D（＊）D（＊） in both the standard （s） （s） model （SM） and the minimal supergravity （mSUGRA） model. Within the considered parameter space, we find that （a） the theoretical predictions for the BRs, CPAs and the polarization fractions in the SM and the mSUGRA model are all consistent with the currently available data within ±2σ errors; （b） For all the considered decays, the supersymmetric contributions in the mSUGRA model are very small, less than 7% numerically. It may be difficult to observe so small SUSY contributions even at LHC.

Performance of rotating detonation engine with stratified injection

In this study,a numerical study based on Euler equations and coupled with detail chemistry model is used to improve the propulsion performance and stability of the rotating detonation engine.The proposed fuel injection called stratified injection functions by suppressing the isobaric combustion process occurring on the contact surface between fuel and detonation products,and thus the proportion of fuel consumed by detonation wave increases from 67%to 95%,leading to more self-pressure gain and lower entropy generation.A pre-mixed hydrogen-oxygen-nitrogen mixture is used as a reactive mixture.The computational results show that the propulsion performance and the operation stability of the engine with stratified injection are both improved,the temperature of the flow field is notably decreased,the specific impulse of the engine is improved by 16.3%,and the average temperature of the engine with stratified injection is reduced by 19.1%.