金属材料的激光相变硬化机理及其工艺参数优化

简要介绍了激光表面技术中的激光相变硬化，阐述了金属材料的激光相变硬化机理，分析了激光功率、扫描速度和光斑直径等工艺参数对硬化效果的影响，指出用激光能量密度描述激光加工工艺参数之间的耦合作用对硬化效果的影响规律。     

激光表面淬火对铬钼铸铁组织和硬度的影响

采用TH-3DC3000型激光加工系统对铬钼铸铁进行了激光表面淬火处理,研究了不同激光功率和扫描速度对铬钼铸铁显微组织、表面硬度及硬化层深度的影响。结果表明,经激光表面淬火后,铬钼铸铁的组织由硬化区、过渡区和基体3个区域组成,硬化区组织为隐晶马氏体、残留奥氏体和球状石墨,过渡区组织为隐晶马氏体、珠光体和球状石墨,基体组织为铁素体、珠光体和球状石墨。在激光表面淬火未对试件产生过热影响时,激光功率的增大和扫描速度的降低均会提升铬钼铸铁的表面硬度和硬化层深度。在5 mm×20 mm的矩形激光光斑下,确定最优的参数组合为激光功率2300 W、扫描速度0.003 m/s,采用该参数组合对铬钼铸铁进行激光淬火处理时,表面硬度为760 HV0.3,硬化层平均硬度为724 HV0.3,硬化层深度可达1.4 mm以上。     

激光强化技术提高模具使用寿命

介绍了用于模具表面的激光强化加工系统和激光强化工艺方法,讨论了激光强化模具表面的硬化层深度和耐磨性能与激光强化工艺参数之间的关系,采用激光强化技术能大幅度提高模具的使用寿命。     

应用激光强化技术提高覆盖件模具寿命

介绍了用于模具表面的激光强化加工系统及常用的强化图形和预处理方法 ,讨论了激光强化模具表面的硬化层深度和耐磨性能与激光强化工艺参数之间的关系 ,采用激光强化模具表面技术能大大延长模具的使用寿命     

模具及模具材料激光相变硬化的初步探索与实验

介绍了激光强化的基础知识,分析了激光固态相变硬化机理,设计并进行了模具及模具材料表面的激光强化实验。将使用计算机模拟得到的指导性参数与使用经验参数获得的强化效果对比,为修正计算机模拟的误差,从而获得激光表面非熔凝淬火的最佳加工参数提供了一个可靠的途径。     

激光表面硬化的特点及在齿轮和模具中的应用优势

本文简要介绍了激光表面硬化的原理、特点、工艺参数、装置系统和经激光表面硬化后金属材料的组织与性能。以激光相变硬化在齿轮和模具中的应用为例表明,采用激光硬化可显著地提高材料的使用性能和寿命。     

激光相变硬化在模具表面强化中的应用研究

介绍了激光表面相变硬化加工的特点及应用、材料表面激光相变硬化工艺的研究———激光相变硬化机制、激光相变硬化层的影响因素、激光相变硬化过程的温度场研究、激光相变硬化后残余应力场的研究以及激光相变硬化在模具表面强化中的应用     

激光相变硬化在模具表面强化中的应用研究

介绍了激光表面相变硬化加工的特点及应用、材料表面激光相变硬化工艺的研究——激光相变硬化机制、激光相变硬化层的影响因素、激光相变硬化过程的温度场研究、激光相变硬化后残余应力场的研究以及激光相变硬化在模具表面强化中的应用。     

45钢表面激光重熔温度场数值模拟

根据传热学理论和数值模拟方法研究温度场的分布规律,在考虑了热物性参数、换热系数、相变潜热随温度变化的因素,应用ANSYS有限元软件的参数化设计语言建立了45钢表面激光重熔连续移动三维瞬态温度场有限元模型。结果表明:提高激光功率对增大相变硬化区效果不大,反而形成较大的熔池而使重熔表面粗糙。与激光功率相比,激光扫描速度对试样温度场的影响较小。经过激光重熔后,形成重熔区、相变硬化区和基体三个区域。实验结果较好地验证了模拟结果,表明所建立的温度场计算模型是正确和可靠的。通过该计算模型,可以掌握金属表面激光重熔过程加热和冷却规律,为制备高性能表面改性层选择合适的工艺参数提供依据。     

模具及模具材料激光相变硬化的初步探索与实验

简要介绍了激光强化的基础知识，分析了激光固态相变硬化机理，设计并进行了模具及模具材料表面的激光强化实验。将使用计算机模拟得到的指导性参数与使用经验参数获得的强化效果对比，为修正计算机模拟的误差，从而获得激光表面非熔凝淬火的最佳加工参数提供了一个可靠的途径。     

人工神经网络技术在成形模具表面激光强化中的应用

基于人工神经网络基本理论，建立在成形模具表面激光强化中预测材料表面最高温度、强化效果的BP网络模型，借助于MATLAB仿真软件中的神经网络工具箱作为开发平台，将试验样本数据和经过试验验证的数值计算结果作为补充的样本数据用于BP网络的训练，利用训练好的BP网络对非线性的样本数据规律进行拟合，实现激光加工工艺参数的优化，为实际生产和加工提供有效的依据。     

基于神经网络的铣削大理石表面粗糙度预测模型

目的利用粒子群优化BP神经网络建立大理石加工表面粗糙度精确预测模型。方法首先采用不同切削参数进行铣削大理石试验,测量加工表面粗糙度值,同时对粒子群算法进行改进,使惯性权重按指数形式递减,并增加速度扰动系数,利用改进粒子群算法优化BP神经网络,建立铣削大理石表面粗糙度神经网络预测模型。其次使用部分试验数据来训练预测模型,使得到的网络参数让网络可以精确预测表面粗糙度。最后利用其余试验数据验证神经网络预测模型的准确性与可靠性。结果经过计算得到粒子群优化BP网络算法的预测模型归一化均方差为0.0501,最大相对误差为10.78%,且误差变化较为均匀。经验公式模型归一化均方差为0.1069,最大相对误差为39.64%,误差变化幅度较大。结论将神经网络模型与经验公式相比较,结果表明,所建网络模型具有较高的预测精度与较强的鲁棒性,对合理选择切削用量以得到理想表面粗糙度有一定参考价值。     

基于RBF神经网络的磨削表面粗糙度预测模型

工件表面粗糙度是反映表面完整性指标中极为重要的一个参数,也是衡量磨削加工质量的重要因素之一,准确地预测磨削表面粗糙度对于快速合理地选择磨削加工工艺参数具有重要意义。通过开展实际磨削实验获得磨削加工数据,对获取的样本数据进行归一化处理以适应RBF神经网络的学习。同时采用循环算法比较得出隐层的最优神经元个数,最终建立了基于径向基函数神经网络的磨削表面粗糙度预测模型,并利用MATLAB进行仿真预测。仿真结果表明:该预测模型准确率很高,能为表面粗糙度预测研究提供可靠数据。     

Simulation of aging process of lead frame copper alloy by an artificial neural network

1　INTRODUCTIONThefunctionsofleadframeinelectronicpackingareprovidingchannelsforelectronicsignalsbetweendevicesandcircuits ,andfixingdevicesoncircuitboards.Leadframealloysarerequiredtohavehighstrengthandgoodformabilityaswellashighelectri calandthermalconductivity .Cu basealloysarethemostpopularleadframealloysandareusedinplasticpackagingapplicationduetotheirhighthermalandelectricalconductivityaswellashighstrength[13] .Theaginghardening processinfabricationofleadframecopperalloymakesitpossi…     

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.     

Surface wavelength content based clustering using neural networks for manufacturing process mapping

Manufacturing processes produce a unique texture on the surface that serves as a fingerprint of the process. It is possible to provide feedback to the process by studying the surfaces carefully. Analytical techniques such as Fourier analysis and digital filters are commonly used to characterize surface profiles. Parameters extracted from filtered profiles are monitored to detect variations in the process. This requires the development of an inference engine to map metrology parameters to manufacturing process parameters. This paper presents an artificial neural network (ANN) based inference engine for providing process feedback with surface finish input. Parameters such as R_a and W_a as well as advanced wavelet based features are extracted from surface finish data collected from a crankshaft manufacturing line and fed as input to the neural network. This input is then clustered using a competitive neural network trained in unsupervised mode. The resulting clusters are analyzed and discussed. The network is then tested with new data to evaluate the quality of the clusters previously generated and to demonstrate the applicability of this technique for detecting process variations.     

A real-time predictive maintenance system for machine systems

This paper describes a novel real-time predictive maintenance system for machine systems based upon a neural network approach. The ability of a neural network to learn non-linear mapping functions has been used for the prediction of machine system parameters using the motion current signature. This approach avoids the need for costly measurement of system parameters. Unlike many neural network based condition monitoring systems, this approach is validated in an off-line proof of concept procedure, using data from an experimental test rig providing conditions typical of those used on production machines. The experiment aims to classify five distinct motor loads using the motion current signature, irrespective of changing tuning parameters. Comparison of the predicted and actual loads shows good agreement. Generation of data covering all anticipated machine states for neural network training, using a production machine, is impractical, and the use of simulated data, generated by an experimentally validated simulation model, is effective. This paper demonstrates the underlying structure of the developed simulation model.     

基于BP和RBF神经网络的表面质量预测研究

目的 研究RBF和BP神经网络在铣削加工中的作用，实现对铣削加工质量的预测，改善铣削性能。方法 对环形铣刀与常用的球形铣刀进行对比，然后基于MATLAB平台，建立以铣削速度、进给量和铣削深度为输入参数，表面粗糙度为输出参数的RBF神经网络模型。通过大量的试验数据对RBF神经网络模型进行训练，然后再用训练好的RBF神经网络模型预测表面粗糙度，将预测值与实测值进行比较，验证RBF神经网络的预测性能。将训练好的BP神经网络模型与RBF神经网络所建模型的预测结果进行比较。结果 发现用RBF方法预测的表面粗糙度相对误差的绝对值不超过6%，最大误差为0.056 098，平均误差为0.022 277，而BP方法的最大误差为0.074 947，平均误差为0.036 578。结论 环形铣刀加工质量更好。RBF神经网络的预测精度较高，具有比BP神经网络更优的预测能力，且拥有建模时间短、收敛速度高、训练过程稳定以及学习速度快等优点，能有效进行铣削质量预测。     

Influence of cyclic temperature changes on the microstructure of AISI 4140 after laser surface hardening

In recent years laser surface hardening using pulsed laser sources has become an increasingly established technology in engineering industry and has opened up wider possibilities for the application of selective surface hardening. However, the choice of the process parameters is generally based on experience rather than on their empirical influence on the resulting microstructure, and for hardening processes with cyclic temperature changes, almost no correlations between process parameters and hardening results are known. Therefore, some problems regarding the choice of the process parameters and their influence on the resulting microstructure still remain. In particular, there is a lack of data concerning the effect of cyclic temperature changes on hardening. To facilitate process optimization, this paper deals with a detailed characterization of the microstructures created in quenched and tempered AISI 4140 (German grade 42CrMo4) steel following a temperature-dependent laser surface hardening treatment. The structure properties were obtained from microhardness measurements, scanning electron microscopy investigations and X-ray diffraction analysis of retained austenite.     

基于遗传算法和神经网络的软脆工件研磨加工工艺智能决策系统

目的解决研磨抛光工艺决策中工艺试验耗时耗力的问题,实现在研磨抛光加工中根据加工工艺参数对加工质量进行预估。方法采用遗传算法优化的BP神经网络为主要算法,构建智能预测模型,建立研磨加工中输入参数和输出参数之间的映射关系。然后收集有效的输入参数和输出参数作为网络训练和测试的样本数据集,通过遗传算法对神经网络的初始化权值和偏置进行优化,用样本数据集训练神经网络。同时,在决策系统的理论基础上,将神经网络与决策系统进行结合,利用神经网络的学习能力建立智能决策的数据库和规则库,最终建立智能决策系统。结果与无改进的BP神经网络的决策方法相比,无论是在预测精度,还是学习速度上,遗传算法优化的神经网络性能更加优异,决策系统的决策效果更好。结论研磨加工工艺智能决策系统是可行的,为研磨加工的工艺决策提供了一种新的思路。