利用神经网络建立快速凝固Cu-Cr-Zr合金时效知识库

根据人工神经网络(ANN)的BP(back propagation)算法，建立了快速凝固Cu-Cr-Zr铜合金时效温度和时间与硬度和导电率的神经网络映射模型。预测值与实际情况吻合良好，硬度和导电率最大误差分别为4．1％和1．9％。通过对样本集的学习，建立了快速凝固时效工艺知识库，对预测和控制该工艺性能非常有益。     

基于人工神经网络的铜合金时效性能研究

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。     

EFFECT OF COLD WORKING ON THE AGING PROPERTIES OF Cu-Cr-Zr-Mg ALLOY BY ARTIFICIAL NEURAL NETWORK

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.     

Prediction and analysis of the aging properties of rapidly solidified Cu-Cr-Sn-Zn alloy through neural network

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.     

Establishing the knowledge repository of rapidly solidified aging Cu-Cr-Zr alloy on the artificial neural-network

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.     

Cu-Cr-Zr合金热处理工艺研究

研究并分析了固溶及时效工艺对Cu-Cr-Zr合金性能的影响，通过性能和工艺对比．得出了理想的固溶和时效工艺参数范围。试验数据表明，加工率对时效Cu-Cr-Zr合金电导率有一定影响，75％左右的塑性加工率，会使Cu-Cr-Zr合金电导率下降3％～5％IACS，因此要合理安排时效处理前后的塑性变形量。试验数据显示．时效前后分别采用30％-40％和〉50％的塑性加工率可以得到良好的综合性能。     

时效工艺对Cu-1.4Cr-0.2Zr-0.04Mg合金硬度和导电率的影响

研究了时效工艺对Cu-1.4Cr-0.2Zr-0.04Mg合金硬度和导电率的影响,并对强化机理和导电率提高的原因做了分析.结果表明:合金经过980℃固溶1 h,450℃时效3 h后其硬度和导电率达到最大值,分别为11 1.5 HBS和77.15%IACS.     

VC-Cu-Cr-Zr复合材料组织与性能研究

根据时效强化和颗粒增强强化的双重强化机制,以Cu-Cr-Zr合金为基体,采用熔铸法制备了VC-Cu-Cr-Zr复合材料。较系统地研究了热处理工艺对VC-Cu-Cr-Zr复合材料组织和性能的影响。结果表明,热处理可明显提高VC-Cu-Cr-Zr复合材料的硬度和电导率,最佳热处理工艺为910℃×1 h固溶处理＋460℃×2.5 h时效     

Cu-Cr-Zr合金热处理工艺研究

研究并分析了固溶及时效工艺对Cu-Cr-Zr合金性能的影响,通过性能和工艺对比,得出了理想的固溶和时效工艺参数范围。试验数据表明,加工率对时效Cu-Cr-Zr合金电导率有一定影响,75%左右的塑性加工率,会使Cu-Cr-Zr合金电导率下降3%￣5%IACS,因此要合理安排时效处理前后的塑性变形量。试验数据显示,时效前后分别采用30%￣40%和>50%的塑性加工率可以得到良好的综合性能。     

Evolutionary artificial neural network approach for predicting properties of Cu- 15Ni-8Sn-0.4Si alloy

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.     

基于人工神经网络与遗传算法的Al-Mg-Si系合金抗拉强度预测模型

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%。     

Cu-0.8Cr-0.2Zr合金固溶时效后的组织与性能研究

通过检测Cu-Cr-Zr合金在固溶时效过程中硬度、电导率的变化及显微组织的观察,研究不同热处理条件对该合金组织和性能的影响。试验结果表明：固溶时效后在合金基体中能得到大量的孪晶组织及细小的析出粒子,在两者综合作用下,合金的组织和性能得到极大的改善。合金在980℃固溶、450℃时效3 h后性能最佳。合金的硬度为111.5 HBS,相对电导率为78.42%IACS。     

时效制度对新型Al-Zn-Mg-Cu合金组织与性能的影响

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。     

基于人工神经网络的7055铝合金二次时效性能预测

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。     

Study on improvement of conductivity of Cu-Cr-Zr alloys

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.     

点焊电极用铜合金材料强化技术的研究现状

在现代工业中广泛应用的点焊电极用铜合金材料多为铬铜、铬锆铜和Al_2O_3弥散强化铜,主要因其导电性、导热性、高温强度、硬度和可加工性。综述了多种强化技术的机理和工艺,通过对基体进行固溶时效和形变强化处理,或进行深冷处理,或通过Al_2O_3、TiB_2、石墨、碳纳米管等对基体进行弥散强化,可以有效地提高铜合金性能。展望了点焊电极用铜合金的强化技术。     

Effect of thermomechanical treatment on microstructure and properties of Cu-Cr-Zr-Ag alloy

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.     

Cu-Cr-Fe-Ni合金形变热处理过程中的组织与性能

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。     

固溶时效工艺对Cu-Ni-Co-Si合金组织和性能的影响

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。     

Cu-Cr合金强化机制研究及其对导电性能的影响

采用真空铸造-固溶处理-冷轧-时效处理工艺制备了Cu-0．8％Cr合金。利用金相显微镜、透射电镜、选取电子衍射、硬度和电导率测试等手段考察了合金在不同处理状态下的组织和性能，定量分析了不同强化方式对合金硬度及导电性能的影响。结果表明：与固溶强化相比，析出强化方法更能有效强化合金，对导电性能影响也更小；形变强化能显著提高合金硬度，但对导电性能几乎无影响；大预变形、短时时效是合金获得较高综合性能的有效手段。