Search results

This paper aims to describe a theoretical and experimental research concerning influence of recess shape on comprehensive lubrication performance of high speed and heavy load hydrostatic thrust bearing with a constant flow.

The lubrication performance of a hydrostatic thrust bearing with different recess shape under the working conditions of high speed and heavy load has been simulated by using computational fluid dynamics and finite volume method.

It is found that the comprehensive lubrication performance of a hydrostatic thrust bearing with circular recess is optimal. The results demonstrate that recess shape has a great influence on the lubrication performance of the hydrostatic thrust bearing.

The simulation results indicate that to get an improved performance from a hydrostatic thrust bearing with constant flow, a proper selection of the recess shape is essential.

Hydrostatic thrust bearing is a key component of the vertical CNC machining equipment, and often results in friction failure under the working condition of high speed and heavy load. The lubricating oil film becomes thin or breaks because of high speed and heavy load and it affects the high precision and stable operation of the vertical CNC machining equipment; hence, it is an effective way of avoiding friction failure for achieving the oil film shape prediction

For the hydrostatic thrust bearing with double rectangular cavities, researchers solve the deformation of the friction pairs in hydrostatic bearing by using the computation of hydrodynamics, elasticity theory, finite element method and fluid-thermal-mechanical coupled method. The deformation includes heat deformation and elasticity deformation, the shape of gap oil film is got according to the deformation of the friction pairs in hydrostatic bearing, and gets the shape of gap oil film, and determines the influencing factors and laws of the oil film shape, and achieves the prediction of oil film shape, and ascertains the mechanism of friction failure. An experimental verification is carried out.

Results show that the deformation of the rotational workbench is upturned along its radial direction under the working condition of high speed and heavy load. However, the deformation of the base is downturned along its radial direction and the deformation law of the gap oil film along the radius direction is estimated; the outer diameter is close but the inner diameter is divergent wedge.

The conclusion can provide a theoretical basis for the oil film control of hydrostatic thrust bearing and improve the stability of vertical CNC machining equipment.

Websites have their own features in aspect preference (e.g. the relative importance platforms place on product aspects in product evaluation). The purpose of this paper is to capture characteristics of different book reviews on aspect preferences by opinion mining techniques.

The authors employ two indicators for identifying aspect preferences, and propose a method for quantifying overall differences of reviews on aspect preferences through three dimensions: aspect awareness, aspect satisfaction and comprehensive value.

The results show that book reviews on e-commerce websites contain information about external aspects of a book (e.g. hardcover), while those on social network websites pay more attention to content-related aspects of the book (e.g. stories). These results indicate that aspect preferences of reviews vary from platforms and make it hard to evaluate book comprehensively based on single-source data. Online book reviews from a wide range of sources can assess book impact from multiple perspectives and dimensions.

In order to illustrate the value of the authors’ method, the authors show book impact assessment based on multi-source data as an application of these difference analyses. Furthermore, the authors present an example of a book promotion to provide customized marketing services for different user clusters.

This study investigates the influence of different data sources on book evaluation from the content of book reviews. The authors also showcase potential applications of these analyses in book impact assessment.

Glass is a promising interposer substrate for 2.5 D integration; yet detailed analysis of the interfacial reliability of through-glass vias (TGVs) has been lacking. The purpose of this paper is to investigate the design and material factors responsible for the interfacial delamination in TGVs and identify methods to improve reliability.

The interfacial reliability of TGVs is studied both analytically and numerically. An analytical solution is presented to show the dependence of the energy release rate (ERR) for interfacial delamination on the via design and the thermal mismatch strain. Then, finite element analysis (FEA) is used to investigate the influence of detailed design and material factors, including the pitch distance, via aspect ratio, via geometry and the glass and via materials, on the susceptibility to interfacial delamination.

ERR for interfacial delamination is directly proportional to the via diameter and the thermal mismatch strain. Thinner wafers with smaller aspect ratios show larger ERRs. Changing the via geometry from a fully filled via to an annular via leads to lower ERR. FEA results also show that certain material combinations have lower thermal mismatch strains, thus less prone to delamination.

The results and approach presented in this paper can guide the design and development of more reliable 2.5 D glass interposers.

This paper represents the first attempt to comprehensively evaluate the impact of design and material selection on the interfacial reliability of TGVs.

In order to improve the accuracy of project cost prediction, considering the limitations of existing models, the construction cost prediction model based on SVM (Standard Support Vector Machine) and LSSVM (Least Squares Support Vector Machine) is put forward.

In the competitive growth and industries 4.0, the prediction in the cost plays a key role.

At the same time, the original data is dimensionality reduced. The processed data are imported into the SVM and LSSVM models for training and prediction respectively, and the prediction results are compared and analyzed and a more reasonable prediction model is selected.

The prediction result is further optimized by parameter optimization. The relative error of the prediction model is within 7%, and the prediction accuracy is high and the result is stable.