神经网络应用于离子渗氮性能预报与工艺优化

以离子渗氮为例,建立了渗层性能预报神经网络模型;通过误差逆传播（BP）网络实现了了逆映射,建立了工艺参数设计的神经网络模块。试验结果验证了性能预报神经网络模型与工艺优化神经网络模型的可靠性,为解决离子渗氮性能预报与工艺优化设计问题提供了一条先进、合理的途径。     

激光淬火在牙嵌电磁离合器热处理技术上的新突破

天津机床电器总厂是生产电磁离合器的专业化生产厂家，热处理工艺是电磁离合器制造过程中的关键工艺之一。长期以来。该工艺对淬火零部件存在着生产效率低，变形量大，废品率高，能耗多，质量不稳定等问题。经过多年的探索、试验、研究．我厂已正式将激光淬火新工艺应用于电磁离合器主要零部件的生产。经实际生产证明．采用激光淬火工艺提高了生产效率，加工零件变形量小；产品质量优良，性能稳定，解决了我厂热处理工艺长期存在的质量问题，收到了明显的技术经济效益。尤其是进入95年以来，我厂成功地将激光淬火工艺引入牙嵌电磁离合器的齿…     

50CrV钢螺丝刀刃口的激光淬火工艺优化与组织性能特征

为提高螺丝刀头刃口硬度与耐磨性,延长其使用寿命,在已做激光淬火薄壁件预试验基础上,采用大功率光纤耦合半导体激光器于螺丝刀刃口上进行激光淬火试验。利用光学显微镜、显微硬度计、摩擦磨损试验仪等试验测试仪器,分析刃口激光淬火区域组织形态特征、显微硬度及耐磨损性能,确定螺丝刀刃口激光淬火可行的工艺参数。试验结果表明:激光淬火后刃口由完全淬透区、过渡区、基材3部分组成,完全淬透区显微组织为针状马氏体与残留奥氏体,过渡区由马氏体与回火索氏体组成。刃口激光淬火合理工艺参数为激光功率600 W、扫描速度900 mm/min。激光淬火后刃口截面平均硬度为805.7 HV0.3,相对淬火前提高了177.4 HV0.3,表层硬度值达到816.7 HV0.3,相对淬火前提高了188.4 HV0.3。淬火后刃口表面磨损量为0.5 mg,为基材磨损量的27.8%,稳定摩擦因数为0.25,为基材稳定摩擦因数的65.8%。激光淬火工艺能有效提高螺丝刀刃口的显微硬度与耐磨性,可用于螺丝刀刃口表面性能强化。     

基于神经网络的双辉离子渗碳性能预报

将人工神经网络理论和算法应用于双辉离子渗碳的研究，在对人工神经网络训练的基础上，建立了双辉离子渗碳工艺与渗层性能预报的数学模型。试验结果验证了性能预报神经网络模型的可靠性，为解决双辉离子渗碳性能预报问题提供了一条先进、合理的途径。     

激光淬火技术在模具表面处理中的应用与展望

简述了通过模具生产零件的优点及其在工业生产中的地位,而且对模具使用寿命的影响因素进行了分析,并对模具失效的主要形式做了简要说明。总结了以往人们对金属进行表面改性几种主要的工艺方法,将激光淬火技术与传统的热处理工艺进行了对比,并指出了激光淬火的优点。总体概括介绍了激光淬火技术的发展历史、激光淬火硬化机理,指出了影响激光淬火工艺的参数和确定三种主要影响因素大小的试验方法,并分析了激光淬火处理后工件不同深度的组织形成过程和相应的各项性能,尤其是对第一层的相变硬化层形成过程中的原子扩散机理作了详细说明。针对提高材料表面吸收激光率而预处理的有效方法及优点进行了论述。简述了激光淬火过程中常用激光器有CO2激光和YAG激光及相应的用途。列举了热作模具、冷作模具、塑料模具各种不同钢经不同的激光淬火参数淬火后,其硬化层的组织和相应的显微硬度,指出了这三种模具钢利用激光淬火的主要目的。最后对激光淬火技术的优缺点作了总结,并对模具表面激光改性的未来进行了展望。     

应用ANSYS5.7软件模拟激光淬火曲轴温度场的研究

介绍了曲轴激光淬火强化技术，采用千瓦级快速轴流式激光器对球铁曲轴进行了激光淬火试验，采用ANSYS5.7有限元分析软件在计算机上再现了曲轴圆角激光淬火过程中内部温度分布的拟实模型，探讨了直接在计算机上优化曲轴圆角激光淬火工艺参数等，分析表明，理论计算结果和实际情况较为符合。     

基于CMAC神经网络的激光切割加工工艺参数的选取

提出了一种用神经网络技术构造激光切割加工工艺参数模型和建立加工参数自动选取系统的方法。利用CMAC神经网络算法，产生一个工艺参数自动选择器，将它嵌入到激光切割加工控制系统中，尤其适用于实时控制选取和改变加工工艺参数，进一步提高控制系统的智能和加工的柔性。     

齿面激光淬火金相分析

本文介绍了用２ｋＷ连续式ＣＯ２激光器进行齿面淬火的工艺，对激光淬火后齿面硬度及其金相组织做了分析，并与高频淬火齿面做了对比，激光淬火齿面组织为理想的针状马氏体，齿面硬度高，疲劳强度高。     

45钢激光淬火工艺的研究

采用1 KW激光器、X-Y工作平台及光学电镜,研究45钢激光淬火工艺过程中的微观组织及性能的变化规律,确定淬火层的硬度指标及优化激光淬火方案。结果表明,当淬火工艺参数为P=143W,V=6mm/s,L=26.0mm,S=0.1 mm时,试验钢淬火后表面形成枝晶,其组织由隐针马氏体和少数残留奥氏体组成,为最优激光淬火方案。     

基于BP神经网络的奥贝球铁的热处理工艺优化

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。     

神经网络与正交试验法结合优化注射工艺参数

结合人工神经网络所表现出来的良好特性,利用正交试验获得的数据作为神经网络的训练样本,建立输入为工艺参数、输出为翘曲变形量的神经网络模型,并通过样本检验了ANN模型的准确性,从而缩短设定工艺参数的时间,在工艺参数取值范围内,采用ANN模型代替CAE软件模拟试验,结合正交试验法,对工艺参数进一步优化。结果表明:将神经网络与正交试验、数值模拟三者结合用于注射过程参数优化可以缩短优化工艺参数的时间,提高工艺设计效率,并能获得比单纯使用正交试验和数值模拟方法更为优化的结果。     

基于正交试验的端帽激光淬火工艺研究

对自动流量平衡阀过流端帽的表面进行激光淬火研究,利用正交试验对影响激光淬火的因素进行了研究,得出激光淬火的最佳参数以及脉冲电流、扫描速度和离焦量对激光表面硬度和淬硬层深度的影响规律.试验结果表明,只要工艺参数选择适当,可获得很好的表面淬火质量,为激光加工提供了理论指导.     

神经网络与正交试验法结合优化板料成形工艺

结合人工神经网络所表现出来的良好特性,利用正交试验获得的数据作为神经网络的训练样本,建立输入为工艺参数、输出为回弹量△Z的神经网络模型.通过样本检验了ANN模型的准确性,从而缩短设定工艺参数的时间,在工艺参数取值范围内,采用ANN模型代替CAE软件模拟试验,结合正交试验法,对工艺参数进一步优化.结果表明,将神经网络与正交试验、数值模拟三者结合用于板料成形参数优化,可以缩短优化工艺参数的时间,提高工艺设计效率.     

Parameter selecting and quality predicting of spot welding based on artificial neural networks

This paper proposes a procedure for using artificial neural networks (ANN) in spot welding, and estabishesspot wilding parameter selecting ANN systems and sopt welding joint quality predicting ANN systems.It has been provedthat the ANN systems have high prediction precision ,providing a new way of parameter selecting and quality predicting in spot welding.     

Analysis of hard-facing appearance of specific powdered superalloys for PTA-coating processes

Artificial neural network (ANN) modeling and multiple linear regression (MLR) analysis have been used to develop a powder hard-facing process using high-energy plasma-transferred (HEPT) heating. HEPT heating can produce coatings with minimal surface roughness. An optimal procedure was developed involving the least number of process parameters but producing the most desirable performance characteristic. The quality characteristic of interest is the surface roughness after HEPT processing, utilizing the “the-smaller-the-better” criterion. Process performance was evaluated with respect to the signal-to-noise ratios, which were obtainable through experiments. The experimental results conclude that ANN models demonstrate a greater accuracy of predicting the surface appearance than the MLR models in terms of prediction error and the coefficient of determination. The results also reveal the most significant process control parameters. The predicted value of powder hard-facing roughness, through the implementation of optimal settings, produces a satisfactory result. The confirmation experiment showed that the ANN method achieved the expected optimal design goals for the HEPT powder hard-facing, thereby justifying the reliability and feasibility of the approach.     

M2高速钢激光表面钴合金化的研究

研究了Ｍ２高速钢激光表面Ｃｏ合金化的组织和性能。着重分析了激光工艺参数对合金化层中Ｃｏ浓度的影响。结果表明，采用激光表面合金化，可使Ｍ２高速钢表面Ｃｏ浓度在２％－５％。合金化层听Ｃｏ浓度主要与激光扫描速度和预沉积的Ｃｏ的厚度有关。退火态的Ｍ２高速钢在激光Ｃｏ合金化后，硬度可达８００ＨＶ。经淬火回火处理后，硬度可达１１５０ＨＶ，明显高于未进Ｃｏ合金化的Ｍ２高速钢。     

An in-process surface recognition system based on neural networks in end milling cutting operations

An in-process based surface recognition system to predict the surface roughness of machined parts in the end milling process was developed in this research to assure product quality and increase production rate by predicting the surface finish parameters in real time. In this system, an accelerometer and a proximity sensor are employed as in-process surface recognition sensors during cutting to collect the vibration and rotation data, respectively. Using spindle speed, feed rate, depth of cut, and the vibration average per revolution (VAPR) as four input neurons, an artificial neural networks (ANN) model based on backpropagation was developed to predict the output neuron-surface roughness R_a values. The experimental results show that the proposed ANN surface recognition model has a high accuracy rate (96–99%) for predicting surface roughness under a variety of combinations of cutting conditions. This system is also economical, efficient, and able to be implemented to achieve the goal of in-process surface recognition by retrieving the weightings (which were generated from training and testing by the artificial neural networks), predicting the surface roughness R_a values while the part is being machined, and giving feedback to the operators when the necessary action has to be taken.     

Intensive quenching Part 1 – What is it?

Abstract

Various intensive quenching processes have been reported since the 1920s. A historical overview of these processes is given. Based on the limited information that has been published, it is likely that many of these systems employed neither intensive quenching processing nor did they produce maximum surface compressive stresses. The objective of the present paper is to define intensive quenching, explaining how it could be used and its processes and advantages.     

Intelligent tool path generation for milling of free surfaces using neural networks

The presented paper has an intention to show how with the help of Artificial Neural Network (ANN), the prediction of milling tool-path strategy could be made in order to establish which milling path strategy or their sequence will show the best results (will be the most appropriate) at free surface machining, according to set technological aim. In our case the best possible surface quality of machined surface was taken as the primary technological aim. Configuration of used Neural Network (NN) is presented, and the whole procedure is shown on an example of mould, for producing light switches. The verification of machined surface quality, according to average mean roughness, R_a, is also being done, and compared with the NN predicted results.     

基于粒子群神经网络的QSn6.5-0.1连铸工艺优化

提出了一种神经网络与粒子群算法相结合的锡磷青铜水平连铸工艺参数优化方法。以水平连铸中7个主要工艺参数为优化对象，带坯成材率为优化目标，进行正交试验并以试验数据作为样本，利用神经网络建立优化参数与优化目标的非线性映射模型。利用粒子群算法对建立的模型进行优化，获得最优铸造工艺参数。选用RBF（径向基函数）神经网络，网络学习采用减聚类算法和最小二乘法，采用惯性权重动态改变策略对粒子群算法进行改进。实际生产证明，经优化的铸造工艺参数使带坯的成材率从56％提高到71％。