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。     

浅谈剪纸艺术在高校景观中的应用

高校校园环境日趋园林化、景观化、生态化,校园景观文化建设正在逐步加强,但是对于民间艺术部分几乎还没有应用,文章正是以此为切入口,把剪纸艺术的表现形式融合在校园景观中,对民间艺术进入高校景观做了一个铺垫,同时为非物质文化遗产的保护和传承寻找了一个更为广阔的空间。通过剪纸的艺术特色,分析剪纸应用在校园景观中不仅美化了环境,同时增加了学生对传统文化的理解,潜意识的唤醒学生对非物质文化遗产的保护。     

玻璃深加工计算机辅助管理系统的研究开发

针对玻璃深加工企业提出的信息化管理需求,通过深入研究分析玻璃深加工流程中需要信息化的各个环节,开发出一个计算机辅助管理系统.系统中本地采用C/S网络体系结构,异地数据通过zlibEx压缩后通过串口实现远程实时安全传输.系统中只需在生成订单时一次输入原始数据,其它功能模块共享原始数据,自动生成各种所需的报表.采用改进的启发式动态寻优方法实现优化切割,改变了手工排料,提高了玻璃原片利用率.该管理系统投入使用后,企业员工的工作效率和经济效益都明显提高,表明了该管理系统的有效性和可行性.     

The generalized assortment and best cutting stock length problems

This paper introduces two new one-dimensional cutting stock models: the generalized assortment problem (GAP) and the best cutting stock length (BSL) problem. These new models provide the potential to reduce waste to values lower than the optimum of current models, under the right management circumstances. In the GAP, management has a standard length and can select one or more of any additional custom stock lengths, and management wishes to minimize cutting stock waste. This model is different from existing models that assume that the selection is from a small fixed set of stock lengths. In the BSL problem, management chooses any number of custom stock lengths, but wishes to find the fewest custom stock lengths in order to have zero waste. Results show waste reductions of 80% with just one custom stock length compared with solutions from standard cutting stock formulations, when item lengths are long relative to the stock length. The models are most effective when the item lengths are nearly as long as the stock length. Solutions from the model have been implemented for a manufacturer. The model is easily generalized to allow multiple existing stock lengths and different costs.