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基于人工神经网络的铜合金时效性能研究 总被引:3,自引:0,他引:3
利用神经网络对Cu-Cr-Zr合金时效温度、时间与硬度和电导率样本集进行学习,采用改进的BP网络算法—Levenberg-Marquardt算法,建立了时效强化工艺BP神经网络模型。预测结果表明:该BP神经网络可以充分挖掘样本蕴含的领域知识,可以对材料性能进行有效预测和分析。 相似文献
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EFFECT OF COLD WORKING ON THE AGING PROPERTIES OF Cu-Cr-Zr-Mg ALLOY BY ARTIFICIAL NEURAL NETWORK 总被引:1,自引:0,他引:1
J.H.Su H.J.Li Q.M.Dong P.Liu B.X.Kang B.H.Tian 《金属学报(英文版)》2004,17(5):741-746
A developmental research has been carried out to deal with the high performance of Cu-Cr-Zr-Mg lead frame alloy by artificial neural network (ANN). Using the cold working to assist in the aging hardening can improve the the hardness and electrical conductivity properties of Cu-Cr-Zr-Mg lead frame alloy. This paper studies the effect of different extent of cold working on the aging properties by a supervised ANN to model the non-linear relationship between processing parameters and the properties. The back-propagation (BP) training algorithm is improved by Levenberg-Marquardt algorithm. A basic repository on the domain knowledge of cold worked aging processes is established via sufficient data mining by the network. The predicted values of the ANN coincide well with the tested data.So an important foundation has been laid for prediction and optimum controlling the rolling and aging properties of Cu-Cr-Zr-Mg alloy. 相似文献
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Su Juan-hua Li He-jun Dong Qi-ming Liu Ping Kang Bu-xi 《Journal of Materials Engineering and Performance》2005,14(3):363-366
It is known that the strength of a metal can be successfully improved by rapid solidification. The hardness of the rapidly
solidified Cu-Cr-Sn-Zn alloy is much higher than that of the solution heat-treated and aged alloy. In this study, multiple-layer,
feed-forward, artificial neural network (ANN) modeling has been used to study the hardness and electrical conductivity behavior
of a rapidly solidified Cu-Cr-Sn-Zn alloy. The ANN model shows how the aging parameters influence the hardness and electrical
conductivity of a rapidly solidified Cu-Cr-Sn-Zn alloy. The ANN modeling also provides encouraging predictions for information
not included in the trained set samples, indicating that a backpropagation network is a very useful and accurate tool for
property analysis and prediction. 相似文献
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Establishing the knowledge repository of rapidly solidified aging Cu-Cr-Zr alloy on the artificial neural-network 总被引:1,自引:0,他引:1
SUJuanhua DONGQiming LIUPing LIHejun KANGBuxi TIANBaohong 《稀有金属(英文版)》2004,23(2):171-175
The non-linear relationship between parameters of rapidly solidified aging processes and mechancal and electrical properties of Cu-Cr-Zr alloy is available by using a supervised artificial neural network (ANN). A knowledge repository of rapidly solidified aging processes is established via sufficient data learning by the network. The predicted values of the neural network are in accordance with the tested data. So an effective measure for foreseeing and controlling the properties of the processing is provided. 相似文献
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Evolutionary artificial neural network approach for predicting properties of Cu- 15Ni-8Sn-0.4Si alloy 总被引:1,自引:0,他引:1
A novel data mining approach, based on artificial neural network(ANN) using differential evolution(DE) training algorithm, was proposed to model the non-linear relationship between parameters of aging processes and mechanical and electrical properties of Cu-15Ni-8Sn-0.4Si alloy. In order to improve predictive accuracy of ANN model, the leave-one-out-cross-validation (LOOCV) technique was adopted to automatically determine the optimal number of neurons of the hidden layer. The forecasting performance of the proposed global optimization algorithm was compared with that of local optimization algorithm. The present calculated results are consistent with the experimental values, which suggests that the proposed evolutionary artificial neural network algorithm is feasible and efficient. Moreover, the experimental results illustrate that the DE training algorithm combined with gradient-based training algorithm achieves better convergence performance and the lowest forecasting errors and is therefore considered to be a promising alternative method to forecast the hardness and electrical conductivity of Cu- 15Ni-8Sn-0.4Si alloy. 相似文献
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为了研究Al-Mg-Si系合金热处理制度和合金成分对力学性能的影响规律,采用人工神经网络(artificial neural network, ANN)和遗传算法(genetic algorithm, GA)相结合的方法,构建了Al-Mg-Si系合金强度预测模型(ANN-GA模型)。通过单因素和双因素分析,研究了合金元素含量和热处理工艺参数对铝合金抗拉强度的影响规律。结果表明,随着Si含量的增加,铝合金的抗拉强度呈现先降低后升高的趋势;随着Mg含量的增加、Cu含量的增加或者Fe含量的减少,铝合金的抗拉强度整体上呈现升高的趋势。双因素分析更能反映输入参数对铝合金抗拉强度的影响。Mg/Si比、Mg+Si总量和时效时间对Al-Mg-Si系合金力学性能的影响显著。铝合金的硬度随时间的变化趋势与ANN-GA模型的计算结果一致,峰值时效时间为29 h,相对误差为11.86%。 相似文献
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采用X射线衍射、金相和扫描电镜等手段,结合力学性能检测和电导率测定,研究了单级时效和双级时效处理对铸态新型Al-Zn-Mg-Cu合金微观组织和综合性能的影响。结果表明:随时效温度的升高和时效时间的延长,晶粒尺寸缓慢增大,电导率逐渐增加。铸态新型Al-Zn-Mg-Cu合金最佳的单级时效工艺为135 ℃×12 h,此时合金的硬度为231.8 HV0.2、抗拉强度为568 MPa、伸长率为2.8%、电导率为33.7%IACS;最佳的第二级时效制度为155 ℃×4 h,此时合金的硬度为216.9 HV0.2、抗拉强度为558.7 MPa、伸长率为4.1%、电导率为35.2%IACS。 相似文献
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基于人工神经网络的7055铝合金二次时效性能预测 总被引:2,自引:0,他引:2
利用人工神经网络对7055铝合金二次时效热处理工艺参数与时效性能样本集进行训练和学习,采用改进的BP网络算法Levenberg-Marquardt算法,建立7055铝合金二次时效热处理工艺BP神经网络模型。针对二次时效工艺特点,研究的工艺参数包括:预时效温度、预时效时间、二次时效温度和二次时效时间。结果表明:神经网络预测值与实验值吻合较好,说明神经网络模型具有较高的精度及良好的泛化能力,可有效地用于预测和分析二次时效工艺参数对7055铝合金时效性能的影响。 相似文献
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Study on improvement of conductivity of Cu-Cr-Zr alloys 总被引:1,自引:0,他引:1
LI Huaqing XIE Shuisheng WU Pengyue MI Xujun 《稀有金属(英文版)》2007,26(2):124-130
The influence of alloying, heat treatment, and plastic working on the performance of Cu-Cr-Zr alloys was investigated. The precipitated phases were characterized as Cr, Cu51Zr14 and Cu5Zr. Cu-Cr-Zr alloys demonstrate combination properties of high strength and high conductivity after solution treatment, aging treatment, and plastic deformation. Precipitation course of Cr is the main factor that influences the conductivity of Cu-Cr-Zr alloys, while adding Zr in the alloys adjusts the orientation relationship between Cr and matrix, and tends to increase the conductivity of aged Cu-Cr-Zr alloys after deformation. 相似文献
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Effect of thermomechanical treatment on microstructure and properties of Cu-Cr-Zr-Ag alloy 总被引:1,自引:0,他引:1
Haofeng Xie Xujun Mi Guojie Huang Baodong Gao Xiangqian Yin Yanfeng Li 《稀有金属(英文版)》2011,30(6):650-656
The effects of thermomechanical treatment on the properties and microstructure of Cu-Cr-Zr alloy and Cu-Cr-Zr-Ag alloy were investigated. Ag addition improves the mechanical properties of the alloy through solid solution strengthening and brings a little effect on the electrical conductivity of the alloy. A new Cu-Cr-Zr-Ag alloy was developed, which has an excellent combination of the tensile strength, elongation, and electrical conductivity reaching 476.09 MPa, 15.43% and 88.68% IACS respectively when subjected to the optimum thermomechanical treatment, i.e., solution-treating at 920°C for 1 h, cold drawing to 96% deformation, followed by aging at 400°C for 3 h. TEM analysis re-vealed two kinds of finely dispersed precipitates of Cr and Cu4Zr. It is very important to use the mechanisms of solid solution strengthening, work hardening effect as well as precipitate pinning effect of dislocations to improve tensile strength of the alloy without adversely affecting its electrical conductivity. 相似文献
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通过硬度和导电率的测量、光学显微镜和透射电镜(TEM),研究了固溶时效工艺对Cu-Ni-Co-Si合金组织和性能的影响。结果表明:经过950 ℃×30 min水淬+500 ℃×480 min随炉冷却后,Cu-Ni-Co-Si 合金得到良好的综合性能:硬度为243.55 HV3,导电率为42.24%IACS;添加少量的V有利于提高二次时效后合金的导电率,并且进行适当的一次时效对提高合金的导电率和硬度是有利的,可以使二次时效试样迅速获得良好的综合性能;Cu-Ni-Co-Si合金的主要强化相为盘状正交结构的δ-(Co,Ni)2Si,过饱和固溶体析出的沉淀物均匀分布,但位错缠结始终存在,其中基体与析出相的位向关系为[001]m//[110]p, (010)m //(001)p。 相似文献
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采用非真空熔炼并经热轧—固溶—冷轧—时效热处理工艺制备Cu-0.59Cr-0.078Fe-0.081Ni合金板,探究热处理和冷变形对合金显微组织、电导率和硬度的影响。结果表明:Cu-Cr-Fe-Ni合金大气熔铸后呈明显的枝状晶组织,经固溶处理后合金发生再结晶,硬度和电导率都相应的降低,分别为65.9 HV0.2、41.7%IACS;经过冷变形处理后合金的硬度显著提高,变形量达90%时,合金的硬度高达144.7 HV0.2;合金变形后在450 ℃时效的过程中硬度先增加后减少,变形量为60%时,时效30 min达到峰时效,此时硬度、电导率分别为155.5 HV0.2、71.4 %IACS。 相似文献