The statistical modeling of surface roughness in high-speed flat end milling

Surface roughness is one of the most important requirements in machining process. The surface roughness value is a result of the tool wear. When tool wear increase, the surface roughness also increases. The determination of the sufficient cutting parameters is a very important process obtained by means of both minimum surface roughness values and long tool life. The statistical models were developed to predict the surface roughness.This paper presents the development of a statistical model for surface roughness estimation in a high-speed flat end milling process under wet cutting conditions, using machining variables such as spindle speed, feed rate, depth of cut, and step over. First- and second-order models were developed using experimental results of a rotatable central composite design, and assessed by means of various statistical tests. The highest coefficient of correlation (R_adj²) (88%) was obtained with a 10-parameter second-order model. Meanwhile, a time trend was observed in residual values between model predictions and experimental data, reflecting the probable effect of the tool wear on surface roughness. Thus, in order to enhance the estimation capability of the model, another independent variable was included into the model to account for the effect of the tool wear, and the total operating time of the tool was selected as the most suitable variable for this purpose. By inserting this new variable as a linear term into the model, R_adj² was increased to 94% and a good fit was observed between the model predictions and supplementary experimental data.In this study, it was observed that, the order of significance of the main variables is as X₅>X₃>X₄>X₁>X₂ (total machining time, depth of cut, step over, spindle speed and feed rate, respectively).     

车载多层减振平台的主动控制及仿真

为了研究被动悬架车载主动减振平台的低成本控制策略及其减振效果,针对一种新型长宽比较大的多层减振平台,建立了车辆被动悬架与主动减振平台的一体化全系统动力学分析模型。进而,设计了"线性被动、旋转主动"的平台控制策略,并应用MATLAB及ADAMS软件,在三种典型路面激励下,进行了仿真测试。结果表明:采用垂向线性振动的多层被动减振、旋转振动的低成本主动控制策略,被动悬架车载减振平台的减振效果明显,实现了车载减振平台的高性价比设计。     

连铸中间包内三维湍流流动的数值模拟

连铸中间包的操作条件决定了其内流动现象的复杂性，为此，本文选择两种不同的湍流模型ｋ－ε和ＬＥＳ（Ｌａｒｇｅｅｄｄｙｓｉｍｕｌａｔｉｏｎ），建立了描述连铸中间包内三维湍流流动的数学模型，并实施了数值计算和模型验证，对单流中间包内的流动现象进行了描述，考察了有，无流动控制情况下中间包内的流动特征和湍流模型的合理性，结果表明，合理的流动控制有利于中间包内非金属夹杂的上浮和吸附，用ｋ－ε模型基本上能够描述     

方柱体镦粗的两个新力学模型

在对平板间镦粗圆柱体的研究［１，２，３］基础上，进一步对普通平板间的方柱体镦粗，进行了工程力学的分析，提出了二个新力学模型，并进行了实验验证。这为形成新的拔长理论［４，６］奠定了力学基础。     

应用人工神经网络理论建立热粘塑性材料的本构关系

本文将人工神经网络（ＡＮＮ）用于建立热粘塑性材料的本构关系，意在探索出一种描述材料变形力学行为的新方法。文中给出了应用人工神经网络建立热粘塑性材料本构关系的ＢＰ模型和学习算法过程，并应用于４５号钢在高温和高速变形条件下的流动应力计算。计算结果与实测结果比较表明，二者吻合良好。因此，应用人工神经网络建立热粘塑性材料的本构关系具有重要的工程应用价值。     

饮用水中天然有机物的分析与表征方法

天然有机物(NOM)是一类分布广泛、成分复杂的有机混合物,可严重影响饮用水的处理效率,并可能对出水水质产生严重的不利影响。因此,为了改进和优化饮用水的处理过程,在研究和实际应用阶段对NOM进行表征和定量以了解其结构特性和反应性具有重要意义。文中先对NOM的富集方法进行了介绍,接着梳理了不同分离方法的依据及作用,最后总结了各类表征方法的原理及应用,以期有助于对NOM和饮用水处理的深入研究。     

计及多能需求的综合能源服务商优化运行策略研究

随着分布式发电技术的不断成熟及发展,未来综合能源服务将是整合不同类型分布式发电并满足用户不同用能需求的有效途径。提出了一种含有多种分布式发电资源同时考虑多用能需求的综合能源服务商优化运行策略模型。首先建立了含有风电、光伏、燃气轮机、电储能、电热泵、辅助锅炉等分布式资源及电、热用能需求的园区综合能源系统优化调度模型;其次计算优化运行后的能源利用效率;最后,分析对比了不同季节、实时电价及天然气价格变化对综合能源服务商运行策略及盈利的影响。仿真结果验证了模型的有效性,其中综合能源服务商的收益对天然气的价格变化更为敏感。     

高风电渗透率下考虑需求侧管理策略的智能微电网调度方法

提出了一种高风电渗透率下考虑需求侧管理策略的智能微电网调度方法。首先,考虑经济、环境成本指标、清洁能源就地消纳量以及可中断负荷和可转移负荷策略,建立了多目标优化调度模型。然后,引入ε约束法生成Pareto前沿解集,利用归一化方法求出最优折衷解。最后,基于24节点配电系统对所提方法进行分析验证。算例结果表明:建立的多目标优化模型能够有效提升清洁能源的消纳率,提高调度方案经济性,同时有效降低发电污染排放。采用的需求侧管理策略能够有效平抑负荷波动,达到削峰填谷的目的。     

The Chick Chorioallantoic Membrane Model: A New In Vivo Tool to Evaluate Breast Cancer Stem Cell Activity

The high plasticity of cancer stem-like cells (CSCs) allows them to differentiate and proliferate, specifically when xenotransplanted subcutaneously into immunocompromised mice. CSCs are highly tumorigenic, even when inoculated in small numbers. Thus, in vivo limiting dilution assays (LDA) in mice are the current gold standard method to evaluate CSC enrichment and activity. The chick embryo chorioallantoic membrane (CAM) is a low cost, naturally immune-incompetent and reproducible model widely used to evaluate the spontaneous growth of human tumor cells. Here, we established a CAM-LDA assay able to rapidly reproduce tumor specificities—in particular, the ability of the small population of CSCs to form tumors. We used a panel of organotropic metastatic breast cancer cells, which show an enrichment in a stem cell gene signature, enhanced CD44⁺/CD24^−/low cell surface expression and increased mammosphere-forming efficiency (MFE). The size of CAM-xenografted tumors correlate with the number of inoculated cancer cells, following mice xenograft growth pattern. CAM and mice tumors are histologically comparable, displaying both breast CSC markers CD44 and CD49f. Therefore, we propose a new tool for studying CSC prevalence and function—the chick CAM-LDA—a model with easy handling, accessibility, rapid growth and the absence of ethical and regulatory constraints.     

Prediction of Functional Consequences of Missense Mutations in ANO4 Gene

The anoctamin (TMEM16) family of transmembrane protein consists of ten members in vertebrates, which act as Ca²⁺-dependent ion channels and/or Ca²⁺-dependent scramblases. ANO4 which is primarily expressed in the CNS and certain endocrine glands, has been associated with various neuronal disorders. Therefore, we focused our study on prioritizing missense mutations that are assumed to alter the structure and stability of ANO4 protein. We employed a wide array of evolution and structure based in silico prediction methods to identify potentially deleterious missense mutations in the ANO4 gene. Identified pathogenic mutations were then mapped to the modeled human ANO4 structure and the effects of missense mutations were studied on the atomic level using molecular dynamics simulations. Our data show that the G80A and A500T mutations significantly alter the stability of the mutant proteins, thus providing new perspective on the role of missense mutations in ANO4 gene. Results obtained in this study may help to identify disease associated mutations which affect ANO4 protein structure and function and might facilitate future functional characterization of ANO4.