Prediction of process parameters of water-assisted injection molding based on inverse radial basis function neural network

Because of the introduction of new processing parameters in water-assisted injection molding (WAIM), processes control has become more difficult. First, design of experiment (DOE) was carried out by using optimized Latin hypercubes (Opt LHS). On the basis of this, computational fluid dynamics (CFD) method was used to simulate and calculate hollowed core ratios and wall thickness differences of cooling water pipe at different positions. Then inverse radial basis function (RBF) neural network model reflecting the fitting relationship between processing parameters and molding quality was established, and accuracy of the model was detected by cross validation. Finally, expected molding quality was applied to predict processing parameters, and the obtained molding quality under the predicted processing parameters was verified by computer aided engineering (CAE) simulation and experimental methods. The results showed that mean relative precisions of processing parameters such as melt temperature, delay time, short shot size, water pressure, and mold temperature for inverse RBF model were 98.6%, 93.6%, 98.5%, 93.9%, and 97.9%, respectively, which met the accuracy requirements. Furthermore, compared with expected values of hollowed core ratios and wall thickness differences, the average errors of CAE and experiment were 2.3% and 4.9%, respectively.     

虽小亦美

本土的业主总是对国外的建筑师充满信任,本土的建筑师也总是对国际建筑大师格外崇敬。     

基于人工神经网络的釉料厚度沉积率模型拟合软件模块的开发与实现

釉料厚度沉积率模型是机器人自动施釉的重要依据。为了进行陶瓷产品的机器人离线编程作业的轨迹自动规划,实现施釉过程中工件釉料厚度的精确性和均匀性,以釉料厚度沉积率模型为研究对象,采用MFC开发了釉料厚度沉积率模型软件功能模块。提出基于神经网络算法拟合沉积模型方案;利用COM形式的VC与MATALB的混合编程实现模型拟合、模型分析功能;设计动态链接库,使模型拟合结果产生word报表。通过试验进行试验验证,结果表明,其最大误差在5um范围之内,从而验证了模型的正确性和有效性。软件界面友好,符合工程实际,提出的方法有助于提高施釉机器人釉料厚度的控制精度,为后续的陶瓷自动施釉离线编程轨迹规划的软件编程和仿真实现提供了具体可操作的模型依据与方法指导。     

卫生陶瓷机器人施釉厚度沉积率模型拟合软件模块的开发

釉料厚度沉积率模型是机器人自动施釉的重要依据.为了对卫生陶瓷产品的施釉机器人离线编程作业轨迹自动规划,实现施釉过程中卫生陶瓷产品釉料厚度的精确性和均匀性.采用MFC开发了釉料厚度沉积率模型功能模块.提出基于遗传算法拟合沉积模型方案,利用COM形式的VC与MATALB的混合编程实现模型拟合、模型分析功能,设计动态链接库,...     

Effect of MWCNTs irradiation grafting treatment on the surface properties of PBO fiber

The aim of this article is improved the surface properties of Poly[p‐phenylenebenzobisoxazole] (PBO) fiber with epichlorohydrin hybridized carboxylic multi walled carbon nanotubes (MWCNTs‐Ecp) grafting by using γ‐ray irradiation technology. The surface chemical properties, the surface morphology, the amount of the grafted MWCNTs on PBO fiber and the surface free energy of PBO fibers have been analyzed. The results show that MWCNTs‐Ecp have been grafted on the surface of PBO fiber by γ‐ray irradiation treatment. The surface chemical inertness and the surface smoothness of PBO fiber are significantly improved by grafting MWCNTs‐Ecp chains, the amount of the grafted MWCNTs on PBO fiber is about 11.9%, and the surface free energy of PBO fiber has an increase of 42.6% by generating some active groups such as ? COOH, ? OH, and ? C? Cl on the surface of PBO fiber. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

ADR-SPLDA: Activity discovery and recognition by combining sequential patterns and latent Dirichlet allocation

This paper presents ADR-SPLDA, an unsupervised model for human activity discovery and recognition in pervasive environments. The activities are encoded in sequences recorded by non-intrusive sensors placed at various locations in the environment. Our model studies the relationship between the activities and the sequential patterns extracted from the sequences. Activity discovery is formulated as an optimization problem in which sequences are modeled as probability distributions over activities, and activities are, in turn, modeled as probability distributions over sequential patterns. The optimization problem is solved by maximization of the likelihood of data. We present experimental results on real datasets gathered in smart homes where people perform various activities of daily living. The results obtained demonstrate the suitability of our model for activity discovery and characterization. Also, we empirically demonstrate the effectiveness of our model for activity recognition by comparing it with two of the widely used models reported in the literature, the Hidden Markov model and the Conditional Random Field model.     

大冶铁矿硬岩排土场复垦的几点作法

国内大型金属露天矿山，多为硬岩边坡、硬岩排土场，尾矿库占地面积也大。大冶铁矿在硬岩排土场条件下进行复垦种植做出了先例，取得了一定的成功经验，对于治理环境收到了初步成效。     

分析右美托咪定静脉泵注在全身麻醉中的应用效果

采用第一性原理计算与实验相结合的方法探究了Cu元素掺杂所造成的元素之间的交互作用对Inconel 718合金Nb偏析的影响。构建了掺杂前后Ni-Fe-Cr-Nb超晶胞模型,计算了掺杂前后各体系的形成热、结合能、态密度、差分电荷密度以及布居分布。计算结果表明,Cu原子的掺杂降低了体系的稳定性;掺杂改变了体系中元素之间的交互作用,影响了原子之间的键合强度及电荷密度分布,Cu的添加增加了基体中Fe原子和Cr原子之间的结合力,但同时也增加了Fe原子和Nb原子之间的排斥力。实验结果表明,微量Cu元素的加入降低了Fe和Cr的偏析,但促进了Nb元素的偏析。第一性原理计算和实验结果表明,Cu掺杂后Nb原子与周围Fe原子间排斥力的增加是Cu促进Nb偏析的本质原因。     

高电压磷酸钴锂正极材料性能调控研究

高性能、高安全储能电池的迫切需求加速了橄榄石型高电压正极材料的研究步伐。为了改善高电压磷酸钴锂(LiCoPO₄)的综合电化学性能,采用溶剂热法研究了溶剂比、过渡元素(Fe、Mn)单掺杂和双掺杂(掺杂元素总量占10%)对正极材料形貌、尺寸、反位缺陷和电化学性能的影响。结果表明,当溶剂比为2.3时,未掺杂LiCoPO₄在0.1C下表现出163.1 mAh·g^-1的最高理论比容量;双掺杂LiCo_0.9Mn_0.05Fe_0.05PO₄正极材料具有突出的循环性能,在0.5C电流密度下,循环100圈后的容量保持率为78.9%,循环稳定性得到明显改善。     

MARTENSITIC SUBSTRUCTURE AND TRANSFORMATION CRACKING IN HIGH CARBON STEEL

本文从马氏体相变的K-S模型出发,求得了片状马氏体内外的相变应力场,确定了片内的塑性应变和真实应力、解释了亚结构的形成,随之导出了马氏体的屈服切变强度τs与亚结构n/N之间的函数关系 τ_S=G/A'[1/2(ε_(11)~*-ε_(22)~*+(2+β_0~2)~(1/2)(cos43.5°/U+cos34.5°sin9°/V)) 由求得的应力和应力强度因子,说明淬火开裂是完全可能的。