一种卷板填充分层递归排样的优化算法

研究了卷板填充排样问题,提出了一种分层递归排样的优化算法。算法使用水平剪切线将卷板分层,每层的宽度和卷板宽度相同,高度和层最左端的主毛坯高度相同;通过调用递归过程确定卷板中层的排列,为各层选定主毛坯,并确定毛坯的排列方式;采用分支定界技术缩小搜索空间。实验结果说明该算法比文献中最近报道的几种算法都有效。     

基于CAN总线的大坝边坡安全监测模块设计

边坡监测是大坝安全监测的一个重要方面,边坡监测的自动化是一种必然的发展趋势。针对边坡监测的特点以及边坡监测常用的传感器的特点,设计了一种基于CAN总线控制系统的大坝边坡安全监测模块。该模块采用C8051f040作为CPU,AD620作为放大器,AD7710作为AD转换芯片,并进行了抗干扰、防雷击、防潮以及环境适应方面的设计。实验证明该模块测量精度能够满足水利系统的要求,而且具有长期稳定性,对环境的适应性等方面则有待该系统在青海拉西瓦大坝的现场实验中得到验证。     

Investigation of process parameters influence in abrasive water jet cutting of D2 steel

In the present experimental study, abrasive water jet (AWJ) cutting tests were conducted on D2 steel by different jet impingement angles and abrasive mesh sizes. The experimental data was statistically analyzed using the simos–grey relational method and ANOVA test. In addition, the outcome of influencing cutting parameters, namely jet pressure, jet impingement angle, and abrasive mesh size on the different response parameters, namely, the jet penetration, material removal rate, taper ratio, roughness, and topography, were studied. Micro-hardness test and surface morphology analysis were employed to examine the D2 cut surfaces at different AWJ cutting conditions. The chemical element study was performed to determine the abrasive particle contamination in the AWJ kerf wall cut surfaces. The ANOVA test result indicated the jet pressure and jet impingement angle as the influencing process parameters affecting the various performance characteristics of AWJ cutting. The overall AWJ cutting performance of the D2 steel has been improved through proper identification of the optimal process parameter settings, namely jet pressure 225?MPa, abrasive mesh size #100, and jet impingement angle 70° by the simos–grey relational analysis.     

Surface integrity studies on abrasive water jet cutting of AISI D2 steel

This article investigates the 3D surface topography and 2D roughness profiles, and micrographs were analyzed in the abrasive water jet (AWJ) cutting of AISI D2 steel kerf wall cut surfaces by varying water jet pressures and jet impact angles. In 3D surface topography, roughness parameters such as Sq, Ssk, Sp, Sv, Sku, Sz, and Sa were improved by various jet impact angles with different water jet pressures. However, the roughness parameters Ssk and Sku strongly depend on the water jet pressure and jet impact angle. This is confirmed by kerf wall cut profile structures. Fine irregularities of peaks and valleys are found on the AWJ cut surfaces, as evident from 2D roughness profiles. The scanning electron microscope micrographs confirm the production of an upper zone not very much damaged and a lower striation free bottom zone, by using the jet impact angle of 70° with a water jet pressure of 200?MPa. Finally, the results indicate a jet impact angle of 70° maintaining the surface integrity of D2 steel better than normal jet impact angle of 90°. The results are useful in mating applications subjected to wear and friction. This has resulted in enhancement of the functionality of the AWJ machined D2 steel components.     

Developing a virtual machining model to generate MTConnect machine-monitoring data from STEP-NC

The ability to predict performance of manufacturing equipment during early stages of process planning is vital for improving efficiency of manufacturing processes. In the metal cutting industry, measurement of machining performance is usually carried out by collecting machine-monitoring data that record the machine tool’s actions (e.g. coordinates of axis location and power consumption). Understanding the impacts of process planning decisions is central to the enhancement of the machining performance. However, current methodologies lack the necessary models and tools to predict impacts of process planning decisions on the machining performance. This paper presents the development of a virtual machining model (called STEP2M model) that generates machine-monitoring data from process planning data. The STEP2M model builds upon a physical model-based analysis for the sources of energy on a machine tool, and adopts STEP-NC and MTConnect standardised interfaces to represent process planning and machine-monitoring data. We have developed a prototype system for 2-axis turning operation and validated the system by conducting an experiment using a Computer Numerical Control lathe. The virtual machining model presented in this paper enables process planners to analyse machining performance through virtual measurement and to perform interoperable data communication through standardised interfaces.     

Cutting force and hole quality analysis in micro-drilling of CFRP

Micro-drilling in carbon fiber reinforced plastic (CFRP) composite material is challenging because this material machining is difficult due to anisotropic, abrasive and non-homogeneous properties and also downscaling of cutting process parameters affect the cutting forces and micro-drilled hole quality extensively. In this work, experimental results based statistical analysis is applied to investigate feed and cutting speed effect on cutting force components and hole quality. Analysis of variance based regression equation is used to predict cutting forces and hole quality and their trend are described by response surface methodology. Results show that roundness error and delamination factor have similar trends to those of radial forces and thrust force, respectively. Non-linear trends of cutting forces and hole quality errors are observed during downscaling of the micro-drill feed value. Optimization results show that cutting forces and hole quality errors are minimum at a feed value which is almost equal to the tool edge radius rather than at the lowest feed value. Therefore, the presented results clearly show the influences of size effects on cutting forces and hole quality parameters in micro-drilling of CFRP composite material.     

Physics-Informed Multistage Machine Learning Strategy for the Nanomachining-Induced Plastic Deformation Behavior

The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high-throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one-step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low-cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high-speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high-quality products.     

白云鄂博铁矿东矿边坡稳定性研究

针对白云鄂博铁矿东矿C区边坡产生的3处不稳定滑体,结合现场工程地质勘察和岩石力学试验,采用理正岩土分析软件5.5版及加拿大岩质边坡稳定性分析计算软件SLIDE5.0对削坡、锚索加固、注浆的二次加固措施进行了分析计算,通过计算得到的安全系数来综合判断该二次加固措施的有效性。分析计算结果表明,通过削坡、锚索加固、注浆的加固措施,可以使白云鄂博铁矿东矿C区边坡最危险滑面的安全系数达到1.186,符合设计边坡稳定安全系数要求。文章最后提出了实施这些措施时的注意事项。