Enhanced symplectic characteristics mode decomposition method and its application in fault diagnosis of rolling bearing
作者：
Cheng, Zhengyang;Wang, Rongji*
期刊：
Measurement ,2020年166:108108 ISSN：0263-2241
通讯作者：
Wang, Rongji
作者机构：
[Wang, Rongji; Cheng, Zhengyang] Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
通讯机构：
[Wang, Rongji] C;Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
关键词：
Calculations;Eigenvalues and eigenfunctions;Roller bearings;Wavelet decomposition;Adaptive signal decomposition;Eigenvalue decomposition;Feature enhancement;Matrix decomposition;Mode decomposition method;Noise reduction methods;Nonstationary signals;Symplectic geometry;Signal processing
摘要：
As an adaptive signal decomposition method, symplectic geometry mode decomposition (SGMD) method is suitable for dealing with non-stationary signals However, the decomposition effect is not ideal when dealing with rolling bearing fault signals with strong background noise. On the one hand, this noise reduction method of SGMD is not suitable for fault signals with strong background noise. On the other hand, SGMD uses QR decomposition method, which results in decomposition error diffusion in the decomposition of singular matrix. Therefore, an enhanced symplectic characteristics mode decomposition (ESCMD) method is proposed in this paper. ESCMD enhances fault features through the calculus operator to make fault features easier to extract, and replaces QR decomposition with eigenvalue decomposition (EVD) to avoid error diffusion during matrix decomposition. Emulational and experimental results show that ESCMD has excellent noise robustness and feature enhancement performance. © 2020
语种：
英文
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基于BP神经网络的奥贝球铁的热处理工艺优化
作者：
邹伟;王荣吉;张立强;俞杰;童希
期刊：
热加工工艺 ,2020年49(6):132-135 ISSN：1001-3814
作者机构：
中南林业科技大学机电工程学院,湖南长沙410000;[张立强; 邹伟; 王荣吉; 童希; 俞杰] 中南林业科技大学
关键词：
BP神经网络;奥贝球铁;热处理工艺优化;拉伸性能
摘要：
以奥贝球铁的一步等温、二步等温淬火温度和一步等温、二步等温淬火保温时间4个工艺参数作为神经网络的输入层参数,以拉伸性能为输出层参数,构建了4×4×1的三层结构的BP神经网络的奥贝球铁热处理工艺优化神经网络模型,并进行了模型的预测和验证。结果表明:该神经网络模型能较好地反映热处理工艺参数与拉伸性能之间的内在规律,BP神经网络预测平均相对误差不超过3.5%,采用BP神经网络对奥贝球铁热处理工艺进行优化,可明显提高奥贝球铁的拉伸性能。
语种：
中文
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辛几何模态分解方法及其分解能力研究
作者：
程正阳;王荣吉;潘海洋
期刊：
振动与冲击 ,2020年39(13):27-35 ISSN：1000-3835
作者机构：
中南林业科技大学机电工程学院, 长沙, 410004;湖南大学机械与运载工程学院, 长沙, 410082;[潘海洋] 湖南大学;[程正阳; 王荣吉] 中南林业科技大学
关键词：
辛几何模态分解(SGMD);辛矩阵相似变换;辛几何分量(SGC);分解能力
摘要：
针对经验模态分解(Empirical Mode Decomposition,EMD)、集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)、局部特征尺度分解(Local Characteristic scale Decomposition,LCD)等方法的不足,提出了一种新的分析方法———辛几何模态分解(Symplectic Geometry Mode Decomposition,SGMD)方法,该方法采用辛矩阵相似变换求解Hamilton矩阵的特征值,并利用其对应的特征向量重构辛几何分量(Symplectic Geometry Component,SGC),从而对复杂信号去噪的同时进行自适应分解,得到若干个SGC。通过仿真信号模型,研究了SGMD方法的分解性能、噪声鲁棒性,分析了分量信号的频率比、幅值比和初相位差对SGMD方法分解能力的影响。将SGMD方法应用于齿轮故障实验数据分析,结果表明SGMD方法能够有效地对待分解信号完成分解并剔除噪声信号。
语种：
中文
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Deep stacking l1-norm center configuration convex hull and its application in fault diagnosis of rolling bearing
作者：
Cheng, Zhengyang;Wang, Rongji*
期刊：
Mechanism and Machine Theory ,2020年143:103648 ISSN：0094-114X
通讯作者：
Wang, Rongji
作者机构：
[Wang, Rongji; Cheng, Zhengyang] Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
通讯机构：
[Wang, Rongji] C;Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
关键词：
Computational geometry;Failure analysis;Fault detection;Learning algorithms;Pattern recognition;Adaptive pattern recognition;Binary classification;Center configuration;Classification performance;Convex hull;Multi-classification;Objective functions;Rolling bearings;Roller bearings
摘要：
Maximum margin classifier with flexible convex hulls (MMC-FCH) is an adaptive pattern recognition method based on convex hull vector and shrinkage factor, which can effectively identify different fault states. However, MMC-FCH is a shallow learning algorithm that cannot effectively diagnose complex signals. Meanwhile, MMC-FCH is essentially a binary classifier. For multi-classification, MMC-FCH can only perform multiple binary classifications. To overcome the shortcomings of MMC-FCH, we propose a deep stacking center configuration convex hull ((DSCH)-H-3), which combines the convex hull with the idea of stacking-based representation learning (SRL). In (DSCH)-H-3, the output of all previous modules combine with the original signal as the input of the next module to fully learn the information in the original signal. At the same time, the concept of center configuration is used to construct the multi-classification objective function of center configuration convex hull ((CH)-H-3). However, redundant information and noise information may still exist in the proposed (DSCH)-H-3 method. Therefore, we further propose a deep stacking l(1)-norm center configuration convex hull (DSl(1)C(3)H) method, which makes the prediction model more robust and sparser under the constraint of l(1)-norm distance. The experiments of rolling bearings show that the proposed DSl(1)C(3)H method has better classification performance. (C) 2019 Elsevier Ltd. All rights reserved.
语种：
英文
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Nearest Neighbor Convex Hull Tensor Classification for Gear Intelligent Fault Diagnosis Based on Multi-Sensor Signals
作者：
Cheng, Zhengyang;Wang, Rongji*
期刊：
IEEE ACCESS ,2019年7:140781-140793 ISSN：2169-3536
通讯作者：
Wang, Rongji
作者机构：
[Wang, Rongji; Cheng, Zhengyang] Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
通讯机构：
[Wang, Rongji] C;Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
关键词：
Gears;Feature extraction;Fault diagnosis;Support vector machines;Intelligent sensors;Gear intelligent fault diagnosis;feature tensor;multi-sensor signals;nearest neighbor convex hull tensor classification;reduction factor
摘要：
For the feature tensor of multi-sensor signals classification problem in gear intelligent fault diagnosis, a new tensor classifier named nearest neighbor convex hull tensor classification (NNCHTC) is proposed in this paper. First, the convex hull distance from a test tensor sample to the convex hull is taken as the similarity measure for classification. Then, the convex hull distance calculation is transformed into the feature tensor inner product, and CANDECOMP/PARAFAC (CP) decomposition is applied to the calculation process to capture the intrinsic information of the feature tensor. Furthermore, the reduction factor is introduced into NNCHTC to enhance its robustness. Finally, feature tensors are obtained from multi-sensor signals by wavelet packet transform (WPT) and used to identify gear working condition by NNCHTC. The experimental results show that NNCHTC not only can be effectively applied to the gear intelligent fault diagnosis based on multi-sensor signals but also has better robustness.
语种：
英文
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兔子爪趾曲面重构及特征曲线的提取
作者：
俞杰;王荣吉;童希;邹伟
期刊：
中国农机化学报 ,2019年40(8):43-47 ISSN：2095-5553
作者机构：
中南林业科技大学机电工程学院,长沙市,410000;[邹伟; 王荣吉; 童希; 俞杰] 中南林业科技大学
关键词：
兔子;仿生;爪趾曲面;曲面重构;特征曲线
摘要：
通过交线构造曲面和点云拟合构造曲面两种方式重构兔子爪趾曲面并分析误差,结果表明第一种方式得到的曲面最大偏差为0.254 mm,后者最大偏差为0.341 mm。相较而言交线构造曲面更适合爪趾等复杂曲面的重构。以交线构造的曲面为基础提取具有特殊结构的脊线和前端轮廓线。拟合得到的脊线方程相关系数为0.999 3,轮廓线方程相关系数为0.996 2。
语种：
中文
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基于蛾火优化的自适应最稀疏时频分析方法及应用
作者：
程正阳;王荣吉;杨兴凯;程军圣
期刊：
噪声与振动控制 ,2019年39(5):185-190 ISSN：1006-1355
作者机构：
中南林业科技大学机电工程学院, 长沙, 410004;湖南大学机械与运载工程学院, 长沙, 410082;[程正阳; 王荣吉] 中南林业科技大学;[杨兴凯; 程军圣] 湖南大学
关键词：
故障诊断;自适应最稀疏时频分析;蛾火优化算法;齿轮
摘要：
自适应最稀疏时频分析(Adaptive and sparsest time-frequency analysis,ASTFA)方法能对复杂信号进行自适 应的分解,但是初始相位函数和带宽参数取值需要人工经验,如果选择不当会严重影响ASTFA方法的分解能力。针对 该问题,论文将蛾火优化(Moth-FlameOptimization,MFO)算法应用于ASTFA方法的初始相位函数和带宽参数的优化, 提出基于蛾火优化的自适应最稀疏时频分析(Moth-flame optimization based adaptive sparsest time-frequency analysis, MFO-ASTFA)方法。将MFO-ASTFA与ASTFA方法进行了对比,并将MFO-ASTFA方法应用于齿轮故障诊断,结果表 明了MFO-ASTFA的优越性及有效性。
语种：
中文
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热处理工艺对Fe-16Mn-1.4C-1.2Al钢组织和硬度的影响
作者：
王亚祥;王荣吉;童希;俞杰
期刊：
热加工工艺 ,2018年47(14):205-207,212 ISSN：1001-3814
作者机构：
[王亚祥; 王荣吉; 童希; 俞杰] 中南林业科技大学机电工程学院, 湖南, 长沙, 410000
关键词：
硬度;热处理;TWIP钢;微观组织
摘要：
设计正交试验, 研究退火温度、保温时间及冷却方式对Fe-16Mn-1.4C-1.2Al系TWIP钢微观组织和显微维氏硬度的影响.结果显示:热处理参数的影响由强到弱依次为退火温度、保温时间、冷却方式.显微硬度随着退火温度升高和热处理时间延长而降低.600℃处于回复阶段, 无退火奥氏体晶粒形核, 硬度下降的主要原因是位错缺陷的减少.1000℃完成回复再结晶阶段, 晶粒粗化, 晶界面积减少, 降低了合金抵抗塑形变形的能力是该阶段硬度下降的主要原因.局部受压时, 晶界抵抗塑性变形的作用要强于位错缺陷的抵抗作用.
语种：
中文
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基于BP神经网络和遗传算法的TWIP钢热处理工艺参数优化
作者：
童希;王荣吉;王亚祥;俞杰;邹伟
期刊：
热加工工艺 ,2018年47(16):176-179 ISSN：1001-3814
作者机构：
[童希; 王荣吉; 王亚祥; 俞杰; 邹伟] 中南林业科技大学机电工程学院, 湖南, 长沙, 410000
关键词：
TWIP钢;BP神经网络;遗传算法;热处理工艺;参数优化
摘要：
为了提高Fe-Mn-C-Al系TWIP钢的力学性能,采用BP神经网络与遗传算法对热处理工艺参数优化。以3个热处理工艺参数为优化对象,以抗拉强度与伸长率之积的强塑积作为优化目标,建立3-4-1的BP神经网络的非线性映射模型,再通过遗传算法的全局寻优,得到具有最优强塑积的热处理工艺参数的最优配置组合。预测结果表明,其最优强塑积热处理工艺为:退火温度为863 ℃、保温时间为26 min、冷却方式为炉冷,并通过试验验证了预测结果的准确性。
语种：
中文
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箱体件消失模铸造工艺设计
作者：
张立强;顾茂华;王荣吉;仲志刚
期刊：
铸造技术 ,2016年37(5):1043-1047 ISSN：1000-8365
作者机构：
中南林业科技大学机电工程学院;湖南大学汽车车身先进设计与制造国家重点实验室;[仲志刚] 湖南晓光汽车模具有限公司;[张立强] 湖南大学;[顾茂华; 王荣吉] 中南林业科技大学
关键词：
数值模拟;消失模铸造;工艺参数设计;ProCAST软件
摘要：
准确的边界条件设置是保证数值模拟精度的必要条件,首先确定合适的边界条件,再结合计算机模拟技术对箱体零件消失模铸造工艺进行分析,最后对比实验与计算机仿真结果验证了建立的有限元模型的准确性。依据模拟分析讨论的结果做了相应的消失模铸造实验,成功地制备出了充型完整的铸件,同时铸件表面品质好,尺寸完整,无缩松和粘砂等铸造缺陷。
语种：
中文
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Modeling the effect of injection molding process parameters on warpage using neural network theory
作者：
Li, Qingchun;Li, Lijun;Si, Xiaojie;Wang, Rongji*
期刊：
JOURNAL OF MACROMOLECULAR SCIENCE PART B-PHYSICS ,2015年54(9):1066-1080 ISSN：0022-2348
通讯作者：
Wang, Rongji
作者机构：
[Li, Qingchun; Si, Xiaojie; Wang, Rongji; Li, Lijun] Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
通讯机构：
[Wang, Rongji] C;Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
关键词：
Backpropagation;Injection molding;Polypropylenes;Predictive analytics;Back propagation artificial neural network (BPANN);Injection molding process;Mold temperatures;Moldflow softwares;Orthogonal design method;Process parameters;Reasonable accuracy;Warpages;Neural networks
摘要：
A back propagation artificial neural network (BPANN) prediction model for warpage of injection-molded polypropylene was developed based on an orthogonal design method. The BPANN model was trained by the input and output data obtained from the moldflow software platform simulations. It is proved that the BPANN model can predict the warpage with reasonable accuracy. Utilizing the BPANN model, the effects of the process parameters, packing pressure (Pp), melt temperature (Tme), mold temperature (Tmo), packing time (tp), cooling time (tc), and fill pressure (pf), on the warpage were investigated. The most important process parameter affecting the warpage was Pp, and the second most important was Tme. The rest of the process parameters, Tmo, tp, tc, and pf, were found to be relatively less influential. Warpage increased with elevating Tmo. In contrast, an increase in Pp and Tme caused the warpage to decrease. © 2015 Taylor and Francis Group, LLC.
语种：
英文
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基于数值模拟的U形件热冲压工艺参数研究
作者：
王荣吉;冯晓欣;张立强;娄宽
期刊：
锻压技术 ,2014年39(5):29-36 ISSN：1000-3940
作者机构：
中南林业科技大学机电工程学院,湖南长沙410004;湖南大学汽车车身先进设计制造国家重点实验室,湖南长沙410082;[娄宽; 冯晓欣; 王荣吉] 中南林业科技大学;[张立强] 湖南大学
关键词：
U形件;热冲压;数值模拟;正交实验设计;工艺参数
摘要：
保压结束后的温度分布对高强钢热冲压零件的组织性能至关重要。以U形件为例,建立热冲压有限元模型,通过基于数值模拟的正交实验讨论了热冲压工艺参数板料成形初始温度、冲压速度、保压时间、摩擦系数对保压结束后U形件最大温差的影响。结论指出:保压时间对保压结束后U形件最大温差的影响显著,延长保压时间可显著降低保压结束后U形件的最大温差;板料成形初始温度显著水平次之;冲压速度与摩擦系数影响较小。同时确定了优化工艺参数组合。在此基础上进行了U形件热冲压试验,模拟结果与试验结果基本吻合,U形件温度变化趋势基本一致,验证了数值模拟的正确性与可靠性。
语种：
中文
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注射成型工艺参数对塑料制品翘曲变形的影响研究
作者：
曾军亮;王荣吉;冯晓欣
期刊：
工程塑料应用 ,2013年41(3):56-59 ISSN：1001-3539
作者机构：
[曾军亮; 王荣吉; 冯晓欣] 中南林业科技大学机电工程学院
关键词：
工艺参数;翘曲变形;交互作用
摘要：
以方形塑料板注射成型工艺为例,以翘曲变形为评价指标,采用Taguchi方法、极差和方差分析方法,优化了模具温度、熔体温度、保压压力和保压时间,获得了最佳工艺参数组合。进行了单因素变动实验和工艺参数交互作用实验,研究了单工艺参数和交互作用对塑料板翘曲变形的影响。结果表明,翘曲变形量随模具温度的增大而增大,随熔体温度、保压压力和保压时间的增大而减小;模具温度和熔体温度、模具温度和保压压力、熔体温度和保压时间的交互作用对翘曲变形影响显著,模具温度和保压时间、熔体温度和保压压力、保压压力和保压时间的交互作用对翘曲变形影响不显著。
语种：
中文
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基于神经网络的工艺参数对翘曲变形和收缩的影响研究
作者：
曾军亮;王荣吉;冯晓欣
期刊：
塑料工业 ,2013年41(7):51-55,72 ISSN：1005-5770
作者机构：
[曾军亮; 王荣吉; 冯晓欣] 中南林业科技大学机电工程学院
关键词：
工艺参数;神经网络;翘曲;收缩
摘要：
摘要：以拉伸和冲击试样（无缺口和有缺口）三种塑料零件的注射成型为例，以翘曲变形和收缩为评价指标，采用Taguchi方法及极差和方差分析方法，优化了模具温度、熔体温度、注塑压力、注塑时间、保压压力、保压时间和冷却时间，获得了最优的工艺参数组合。建立了神经网络模型，利用神经网络的预测功能，预测出变动单个工艺参数下的翘曲变形量和收缩率，研究了单个工艺参数对翘曲变形和收缩的影响，以指导生产实践。
语种：
中文
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An intelligent system for low-pressure die-cast process parameters optimization
作者：
Zhang, Liqiang;Wang, Rongji*
期刊：
International Journal of Advanced Manufacturing Technology ,2013年65(1-4):517-524 ISSN：0268-3768
通讯作者：
Wang, Rongji
作者机构：
[Zhang, Liqiang; Wang, Rongji] Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.;[Zhang, Liqiang] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China.
通讯机构：
[Wang, Rongji] C;Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
关键词：
LPDC;Process parameters;Artificial neural network;Genetic algorithm;Numerical simulation
摘要：
Low-pressure die-cast (LPDC) is widely used in manufacturing thin-walled aluminum alloy products. Since the quality of LPDC parts are mostly influenced by process conditions, how to determine the optimum process conditions becomes the key to improve the part quality. In this paper, a combining artificial neural network and genetic algorithm (ANN/GA) method is proposed to optimize the LPDC process. In this method, considering the more complicated preparation process of thin-walled casting, an ANN model combining learning vector quantization and back-propagation (BP) algorithm is proposed to map the complex relationship between process conditions and quality indexes of LPDC. Meanwhile, the orthogonal array design and numerical simulation is applied to obtain the training samples instead of carrying out a real experiment for the sake of cost saving. The genetic algorithm is employed to optimize the process parameters with the fitness function based on the trained ANN model. Then, by applying the optimized parameters, a thin-walled component of 300 mm in length, 100 mm in width, and 1.5 mm in thickness is successfully prepared. The results indicate that the proposed intelligent system is an effective tool for the process optimization of LPDC.
语种：
英文
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基于神经网络和遗传算法的工艺参数优化
作者：
曾军亮;王荣吉;冯晓欣
期刊：
塑料 ,2013年42(5):106-109 ISSN：1001-9456
作者机构：
[曾军亮; 王荣吉; 冯晓欣] 中南林业科技大学机电工程学院
关键词：
工艺参数;神经网络;遗传算法;优化
摘要：
以翘曲变形和收缩为质量指标,采用正交试验法、神经网络模型和遗传算法,优化了模具温度、熔体温度、注塑压力、注塑时间、保压压力、保压时间和冷却时间,获得了工艺参数的最优配置组合,提高了制品质量.利用最优配置组合的工艺参数进行了注塑成型试验,并通过测量验证了CAE模拟的正确性.
语种：
中文
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Effects of squeeze casting parameters on solidification time based on neural network
作者：
Wang, Rong Ji* ;Tan, Wen Fang;Zhou, Dian-Wu
期刊：
INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY ,2013年46(2-3):124-140 ISSN：0268-1900
通讯作者：
Wang, Rong Ji
作者机构：
[Wang, Rong Ji; Tan, Wen Fang] Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.;[Zhou, Dian-Wu] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China.
通讯机构：
[Wang, Rong Ji] C;Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
关键词：
Dies;Heat transfer coefficients;Neural networks;Pressure pouring;Squeeze casting;Applied pressure;Casting parameters;Critical value;Influence of process parameters;Interfacial heat transfer coefficients;Pouring temperatures;Process parameters;Solidification time;Solidification
摘要：
Based on artificial neural network (ANN) and ProCast software, the effects of different process parameter on the solidification time of squeeze casting hot die steel were investigated, such as interfacial heat transfer coefficient of metal/cavity die (h1), applied pressure (Pa), interfacial heat transfer coefficient of metal/male die (h2), die pre-heat temperature (Td) and pouring temperature (Tp). An ANN model on the relationship between process parameters and solidification time was constructed. The test results show that the ANN model is reasonable and can accurately predict the solidification time and the influence of process parameters on solidification time. The most important parameter is Td, and the secondary is Tp. While Td and Tp increasing within a certain range, the solidification time is found to increase, in contrast, Pa causes the solidification time to decrease. However, h1 and h2 increasing within a certain range, the solidification time is found to decrease. Moreover, the solidification time increases rapidly when h1 and h2 are above their respective critical point. The critical value increases with an increase in mould thickness. Copyright © 2013 Inderscience Enterprises Ltd.
语种：
英文
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A back propagation artificial neural network prediction model of the gate freeze time for injection molded polypropylenes
作者：
Wang, Rongji* ;Feng, Xiaoxin;Xia, Yuejun;Zeng, Junliang
期刊：
JOURNAL OF MACROMOLECULAR SCIENCE PART B-PHYSICS ,2013年52(10):1414-1426 ISSN：0022-2348
通讯作者：
Wang, Rongji
作者机构：
[Feng, Xiaoxin; Wang, Rongji; Zeng, Junliang; Xia, Yuejun] Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha, Hunan, Peoples R China.
通讯机构：
[Wang, Rongji] C;Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha, Hunan, Peoples R China.
关键词：
artificial neural network;gate freeze time;plastic injection molding
摘要：
This paper presents a back propagation artificial neural network (BP ANN) prediction model of the gate freeze time (tgf) for injection molded polypropylenes. An orthogonal design method was applied to enhance the BP ANN performance. The test results on the performance of the BP ANN prediction model showed that it can predict tgf with reasonable accuracy. Utilizing the BP ANN prediction model, the effects of the process factors, melt temperature (Tme), fill time (tf), gate area (A in), packing pressure (Pp), and mold temperature (T mo) on tgf were investigated. The simulation results showed that the most important process factor affecting tgf was Tme, followed by tf, Ain, and Pp, with Tmo having the least effect. The gate freeze time increased with elevated Tme, tf, Ain, and Pp. © 2013 Copyright Taylor and Francis Group, LLC.
语种：
英文
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Evaluation of effect of plastic injection molding process parameters on shrinkage based on neural network simulation
作者：
Wang, Rongji* ;Zeng, Junliang;Feng, Xiaoxin;Xia, Yuejun
期刊：
JOURNAL OF MACROMOLECULAR SCIENCE PART B-PHYSICS ,2013年52(1):206-221 ISSN：0022-2348
通讯作者：
Wang, Rongji
作者机构：
[Feng, Xiaoxin; Wang, Rongji; Zeng, Junliang; Xia, Yuejun] Cent S Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
通讯机构：
[Wang, Rongji] C;Cent S Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
关键词：
Cooling time;Crystallinities;Fill pressure;Melt temperature;Mold temperatures;Moldflow;Neural network simulations;Packing pressure;Plastic injection molding;Process parameters;Reasonable accuracy;C (programming language);Molds;Neural networks;Polypropylenes;Research laboratories;Shrinkage
摘要：
The effects of process parameters, mold temperature (T mo), melt temperature (T me), cooling time (t c), fill pressure (P f), packing pressure (P p), and packing time (t p) on the shrinkage of injection molded polypropylene were investigated by utilizing a combination of the Artificial Neural Network (ANN) method and Moldflow software. An ANN model is developed to understand the relationship between plastic injection molding process parameters and shrinkage. The test results on the performance of the ANN model show that it can predict the shrinkage with reasonable accuracy. The simulation results show that the most important process parameter affecting shrinkage is Pp, followed by T me and T mo, with t c, P f, and t p having the least effect. Shrinkage increases with the elevated T mo and tc. In contrast, the increases in Pp,T me,t p, and P f cause shrinkage to decrease. The strongest effect on the shrinkage is the amount of material forced into the mold, followed by the crystallinity and orientation of the material. © 2013 Copyright Taylor and Francis Group, LLC.
语种：
英文
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Estimation of heat flux at metal-mold interface during solidification of cylindrical casting
作者：
Zhang, L. Q.;Wang, R. J.*
期刊：
International Journal of Material Forming ,2013年6(4):453-458 ISSN：1960-6206
通讯作者：
Wang, R. J.
作者机构：
[Wang, R. J.; Zhang, L. Q.] Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.;[Zhang, L. Q.] Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China.
通讯机构：
[Wang, R. J.] C;Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha 410004, Hunan, Peoples R China.
关键词：
Commercial software;Cylindrical geometry;Flux boundary conditions;Heat conduction models;Interfacial heat flux;Inverse heat conduction model;Metal-mold interface;Solidification process;Computer software;Cylinders (shapes);Heat conduction;Molds;Solidification;Heat flux
摘要：
For modeling solidification process of casting accurately, the correct information about the heat flux boundary condition is required. In this study, an inverse heat conduction model is established to determine the interfacial heat flux at metal-mold in the process of casting with a cylindrical geometry. The numerically calculated temperature is compared with the exact solution and simulation solution obtained by commercial software ProCAST to investigate the accuracy of forward heat conduction model. The analysis of calculated heat flux indicates that the inverse model may be taken as a feasible and effective tool for the estimation of the metal-mold interfacial heat flux during solidification of cylindrical casting. © 2012 Springer-Verlag France.
语种：
英文
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