Multi-Objective Optimization of Fused Deposition Modeling for Mechanical Properties of Biopolymer Parts Using the Grey-Taguchi Method

The urgent need to develop customized functional products only possible by 3D printing had realized when faced with the unavailability of medical devices like surgical instruments during the coronavirus-19 disease and the ondemand necessity to perform surgery during space missions.Biopolymers have recently been the most appropriate option for fabricating surgical instruments via 3D printing in terms of cheaper and faster processing.Among all 3D printing techniques,fused deposition modelling(FDM)is a low-cost and more rapid printing technique.This article proposes the fabrication of surgical instruments,namely,forceps and hemostat using the fused deposition modeling(FDM)process.Excellent mechanical properties are the only indicator to judge the quality of the functional parts.The mechanical properties of FDM-processed parts depend on various process parameters.These parameters are layer height,infill pattern,top/bottom pattern,number of top/bottom layers,infill density,flow,number of shells,printing temperature,build plate temperature,printing speed,and fan speed.Tensile strength and modulus of elasticity are chosen as evaluation indexes to ascertain the mechanical properties of polylactic acid(PLA)parts printed by FDM.The experiments have performed through Taguchi’s L27orthogonal array(OA).Variance analysis(ANOVA)ascertains the significance of the process parameters and their percent contributions to the evaluation indexes.Finally,as a multiobjective optimization technique,grey relational analysis(GRA)obtains an optimal set of FDM process parameters to fabricate the best parts with comprehensive mechanical properties.Scanning electron microscopy(SEM)examines the types of defects and strong bonding between rasters.The proposed research ensures the successful fabrication of functional surgical tools with substantial ultimate tensile strength(42.6 MPa)and modulus of elasticity(3274 MPa).

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Direct Pointwise Comparison of FE Predictions to StereoDIC Measurements:Developments and Validation Using Double Edge-Notched Tensile Specimen

To compare finite element analysis(FEA)predictions and stereovision digital image correlation(StereoDIC)strain measurements at the same spatial positions throughout a region of interest,a field comparison procedure is developed.The procedure includes(a)conversion of the finite element data into a triangular mesh,(b)selection of a common coordinate system,(c)determination of the rigid body transformation to place both measurements and FEA data in the same system and(d)interpolation of the FEA nodal information to the same spatial locations as the StereoDIC measurements using barycentric coordinates.For an aluminum Al-6061 double edge notched tensile specimen,FEA results are obtained using both the von Mises isotropic yield criterion and Hill’s quadratic anisotropic yield criterion,with the unknown Hill model parameters determined using full-field specimen strain measurements for the nominally plane stress specimen.Using Hill’s quadratic anisotropic yield criterion,the point-by-point comparison of experimentally based full-field strains and stresses to finite element predictions are shown to be in excellent agreement,confirming the effectiveness of the field comparison process.

Grey GRM(1, 1) Model Based on Reciprocal Accumulated Generating and Its Application

Aiming the problem of low accuracy during establishing grey model in which monotonically decreasing sequence data and traditional modeling methods are used, this paper applied the reciprocal accumulated generating and the approach optimizing grey derivative which is based on three points to deduce the calculation formulas for model parameters, established grey GRM(1, 1) model based on reciprocal accumulated generating. It provides a new method for the grey modeling. The example validates the practicability and reliability of the proposed model.

基于DBSVDD-RVR的多模态间歇过程质量变量在线软测量

现有的多模态间歇过程软测量未考虑过程数据的批次差异及过渡模态的复杂时变特性,影响了间歇过程模态识别的合理性及质量变量在线软测量的准确性。提出了一种基于双边界支持向量数据描述-相关向量回归(double boundary support vector data description-relevance vector regression,DBSVDD-RVR)的间歇过程质量变量在线软测量方法。依据间歇过程离线模态划分获得的各稳定及过渡模态历史数据,建立DBSVDD在线模态识别模型,并引入滑动窗,构建间歇过程在线模态识别策略,利用DBSVDD模型实现在线测量数据的模态识别;在此基础上,构建了基于超球体距离的数据相似度计算方法,选择过渡模态在线数据的相似建模数据集,建立过渡模态的即时学习RVR软测量模型,并依据历史数据建立各稳定模态的RVR软测量模型,实现间歇过程质量变量的在线软测量。青霉素发酵过程的实验结果表明,所提方法有效地提高了间歇过程模态识别的合理性和质量变量在线软测量的准确性。

基于CIM的智慧园区技术探索与落地实践

城市信息模型(CIM)是我国智慧城市建设及城市数字化转型的重要抓手之一。其核心技术主要包括物联感知技术、GIS与新型测绘技术、孪生建模技术、可视化渲染技术、仿真推演技术和交互控制技术等。本文以独角兽岛智慧园区项目为例,探讨CIM技术、物联网技术、大数据技术及可视化渲染技术的交汇与融合应用,设计并建设应用场景支撑产业园区全生命周期的规划、建设、管理、运营以及园区空间价值提升,形成园区、产业、市场和社会的数字化运行理念和方式,并提炼出项目中展现出的特色与亮点,为推动我国智慧园区及智慧城市的建设提供基础支撑和建设经验。

基于兴趣点(POI)数据的产业链创新链空间布局及空间匹配性分析——以浙江省为例

基于POI数据,构建引入了服务能力和类别差异、服务半径和距离衰减效应的改进潜力模型,以浙江“415”先进制造业集群为样本测度产业链创新链的空间布局及空间匹配性。结果表明,世界级先进产业集群相比特色产业集群、传统优势产业相比新兴产业与创新链空间匹配性更高,省级相比国家级、基础研究和成果转化环节相比其他环节空间匹配性更高,且各集群在不同层级、环节上的空间匹配性差异较大。为此,从优化创新平台载体空间布局、重点产业“一群一策”及不同产业间强化协同方面提出了推进“双链”深度融合的相关建议。

基于数据挖掘的新旧动能转换成效监测体系研究

采用目前方法构建新旧动能转换成效监测体系时,没有对监测数据进行去噪处理,导致方法的监测精度和敏感度较低。为此,提出基于数据挖掘的新旧动能转换成效监测体系研究方法。首先确定构建新旧动能转换成效监测体系的具体步骤,其次在双树复小波原理的基础上对监测数据进行去噪处理,利用数据挖掘方法构建新旧动能转换成效监测指标体系,并对监测指标进行标准化处理,最后结合熵权法和非线性加权模型完成新旧动能转换成效的监测。实验结果表明所提方法的监测精度和敏感度较高。

基于风力预测的输电线路风偏放电预警

为提高对强对流环境下输电线路风险的预警精度,研究了输电线路风偏放电预警方法。首先,基于气象雷达的历史监测数据,构建双层串联SVM分类器,生成强对流风力预测模型。其次,构建输电线路风偏几何模型,分析非水平风对输电线路的影响,推导临界状态下风偏角和风偏风速的计算公式。最后,采用广义极值分布函数计算输电线路风偏放电概率,发布风偏放电风险预警等级。结果表明,基于气象雷达数据和强对流风力预测的输电线路风偏放电预警方法有效、可靠,可为强对流天气下的输电线路维护、电网运行、防灾管理提供决策参考。

Slice Data Based Support Generation Algorithm for Fused Deposition Modeling

This paper presents a robust algorithm to generate support for fused deposition modeling (FDM). Since many flaws appear in most stereo lithography (STL) models, this algorithm utilizes slice data as input. A top-down approach was used to calculate the support slice layer by layer. The generation algorithm was described in detail including the slice grouping, oriental bounding box (OBB) calculation, offsetting, and Boolean operations. Several cases are given to validate the efficiency and robustness of the procedure. The algorithm provides necessary support not only for hanging surface but also for hanging vertexes and edges with O(n) time complexity, where n is the number of layers. The algorithm fully utilizes the parts’ self-support ability and reduces support volume to the maximum extent. This slice data based algorithm has the same efficiency as the STL based algorithm but is more stable, which significantly enhances the robustness of the support generation process.

Non-equal-interval direct optimizing Verhulst model that x(n) be taken as initial value and its application

To overcome the deficiencies of the existing Verhulst GM(1,1) model, based on the existing grey theory, a non-equal-interval direct optimum Verhulst GM(1,1) model is built which chooses a modified n-th component x(n) of X(0) as the starting condition of the grey differential model. It optimizes a modified β value and the background value, and takes two times fitting optimization. The new model extends equal intervals to non-equal-intervals and is suitable for general data modelling and estimating parameters of the direct Verhulst GM(1,1). The new model does not need to pre-process the primitive data, nor accumulate generating operation (AGO) and inverse accumulated generating operation (IAGO). It is not only suitable for equal interval data modelling, but also for non-equal interval data modelling. As the new information is fully used and two times fitting optimization is taken, the fitting accuracy is the highest in all existing models. The example shows that the new model is simple and practical. The new model is worth expanding on and applying in data processing or on-line monitoring for tests, social sciences and other engineering sciences.

改性复合注浆材料的制备及精准注浆加固测量试验

注浆加固是解决电力工程结构不稳最常用的一种方法,但电力工程具有较高的隐蔽性和复杂性,针对隐蔽工程中注浆加固的精准性的问题,提出一种实时监测的双液注浆加固工程模型试验。首先设计并制作了双液注浆模型装置,以TDS-630数据采集仪为主要设备,对装置上布置的测试元件参数进行监测和分析。研究结果表明,浆液在模型内部的扩散模式为非均匀扩散,在注浆10 min时,最大温度值和最大压力值分别为96.5℃和8.4 MPa,在注浆15 min时,模型中部位置出现最大应力值95.8 MPa。

创新型城市建设能够深化数字全球价值链参与水平吗?

将创新型城市建设视为一项“准自然”实验,基于2008—2021年中国272个地级及以上城市面板数据,应用多时点双重差分模型考察创新型城市建设对数字全球价值链参与的影响。研究发现:整体来看,创新型城市建设可提升数字全球价值链参与水平,这一结果在经过一系列稳健性检验后依然成立。异质性检验结果表明,创新型城市建设对中小规模、沿海区域及低市场化程度城市参与数字全球价值链的作用效果更为显著。作用机制检验结果表明,创新型城市建设主要通过缓解要素结构错配、前沿数字技术追赶以及服务贸易结构升级对数字全球价值链参与产生推动作用。基于上述结论,提出全面深化创新型城市战略点布局、推动城际数字经济联动格局建设、统筹构建城际数据统一要素市场的政策建议,为高水平建设创新型城市,提升国际数字价值链地位提供新思路。

成渝地区双城经济圈高速铁路和高速公路建设对县域城镇化发展的影响研究

完善交通基础设施建设是促进城镇化发展的重要措施,为推进成渝地区双城经济圈县域城镇化进程,对交通影响效应的科学评价必不可少。基于2000—2020年成渝地区双城经济圈县级城市面板数据,以夜间灯光数据作为县域城镇化的代理变量,通过构建静态和动态的双重差分模型,估计并分析高速铁路和高速公路建设对县域城镇化发展的影响。结果显示,高速铁路建设对发展较好的县域城市影响较小,短期内不利于相对落后县域城市的进一步发展;高速公路在开通后短时间内体现出对县域城镇化显著的正向促进作用,且城市发展水平越高,高速公路对县域城镇化的正面效应越显著。研究结果为进一步调整高速铁路和高速公路规划建设思路,实现县域城镇化和交通协同发展的政策制定提供理论依据。

基于模型-数据联合驱动的电网扰动定位方法

快速准确的电力系统扰动定位是实施及时有效的控制措施的重要前提。广域同步相量量测为快速准确的扰动定位提供了数据基础,同时刻画扰动传播的连续体动力学模型为扰动定位提供模型支撑。结合同步相量测量单元(phasor measurement unit,PMU)量测数据和扰动传播模型,该文提出一种基于模型–数据联合驱动的电网扰动定位方法。首先通过对PMU量测数据进行奇异谱分析和计算累计平方和指标来实现扰动检测。进而在部分PMU确定的子拓扑内,分别通过量测数据和传播模型提取二维时空特征。最后根据扰动传播过程中时空特征之间的相关性,提出基于度量学习的双通道三元孪生网络的扰动定位方法,仅利用部分PMU量测信息即可快速定位扰动位置,为后续抑制扰动的主动控制策略的投切奠定基础。基于IEEE 10机39节点系统,验证所提方法的有效性和鲁棒性。

苏皖地区强降水过程双雨带漏报原因分析

基于降水量观测数据、ECMWF模式降水预报数据和ERA5再分析数据,研究了2022年3月19—20日苏皖地区一次降水过程中双雨带漏报个例,并对比分析了一次双雨带成功预报个例.结果表明:①漏报个例的北雨带中,16个站降水量进入近30年3月日降水量前10名,两个站降雨量刷新3月份的历史极值,是一次极端性较强的降水过程.②ECMWF模式未能准确预报北雨带冷平流,未反映实况中出现的对流活动,且预报的对流层低层相对湿度始终偏小,两个原因直接导致北雨带降水量预报值显著偏少.③成功预报个例中,过程开始阶段,ECMWF模式有700hPa高湿区预报范围偏大、850hPa高湿区预报位置偏南的情况,以后逐渐调整为与实况一致,较好地预报了苏皖地区对流层低层湿度条件,佐证了湿度预报偏差是造成雨带预报偏差的重要原因.

基于极致梯度提升和支持向量机的高速公路服务区供暖系统健康状态评估

为保障高速公路服务区供暖系统的健康运行,本文结合供暖系统的构成和运行特征,设计了一种用于评估服务区供暖系统运行健康状态的极致梯度提升(Extreme Gradient Boosting,XGBoost)和支持向量机(Support Vector Machine,SVM)的双层机器学习模型,并将系统运行健康状态分为健康、亚健康、故障和异常四个等级。首先,分析电压、电流、水温、机组运行状态、蓄热泵、循环泵和泵运行状态7个子系统的运行特征,使用随机函数和欠采样处理构建特征向量集。其次,采用XGBoost构建下层各子系统健康状态评估模型,并输出各子系统的健康状态。然后,融合各子系统健康状态的评估结果,输入至上层SVM系统评估模型,得到整个供暖系统的健康状态。最后,将实验与决策树、AdaBoost和XGBoost等模型进行对比,结果表明所提出的XGBoost+SVM双层机器学习模型相较对比模型而言,不仅判断精度更高,同时还可评估整个供暖系统以及各子系统的健康状态。

数字技术能否助力科技创新?--基于国家级大数据综合试验区的分析

以大数据引领推动科技创新是实施大数据战略行动的关键一环。基于2007-2019年的中国城市面板数据,以国家级大数据综合试验区为准自然实验,从理论和实证两方面,通过多期双重差分模型,系统分析数字技术对科技创新的影响。为证明结果的可靠性,进行一系列稳健性检验。研究结果显示,提升数字技术水平会显著促进科技创新水平,且数字技术的作用强度具有区域异质性,呈现南部地区>北部地区、高行政等级城市>一般地市的情况。基于以上结果,提出对策建议:进一步推动大数据试验区建设,优化创新制度环境,完善人才流动机制,充分发挥数字技术对科技创新的促进作用。

基于大数据与双积分政策的产品规划

在整个汽车生态系统研究中,政策对于产品规划影响巨大,车企发展的基准依托于国家政策,任何车企要想健康长久地发展,离不开国家政策的支持,因此,研究政策因素对于未来车企的发展规划具有重要的意义,但是在传统的新能源汽车产品规划中,并没有考虑政策因素的影响,因此,文章基于大数据的角度并结合国家双积分政策开展了研究,实现了从政策角度构建利润模型,进而提出未来的产品规划意见。首先利用灰色时间序列函数模型对汽车销量及产量进行预测;其次结合预测数据及国家有关于双积分政策的相关规定建立新能源汽车双积分政策补贴利润模型;最后选取上汽通用五菱的代表车型五菱宏光MiniEV进行案例分析,并依据利润模型对五菱未来新能源汽车产品提出规划意见。通过案例分析验证了该模型的适用性,具有一定的实用意义。

基于证据合成规则的多模型软测量

针对传统软测量方法存在的预测性能差、融合能力低和适应性不强等缺点,本文提出了一种基于证据(D–S)合成规则的多模型软测量方法.首先,利用仿射传播(AP)聚类方法和最小二乘支持向量机(LS–SVM)建立多个子模型;然后,利用D–S合成规则得到多个证据概率分配函数,将其作为权值因子对子模型输出进行融合得到多模型的输出,提高了模型的预测能力和融合能力;最后,将上述方法用于非线性系统和酯化率的软测量建模,仿真结果表明,相比于单一模型和传统的多模型软测量方法,本文方法具有更好的预测性能和精度,是一种有效的软测量方法.