端淬曲线非线性方程法模拟模型中合金化当量公式的修正

采用试错法、数理统计分析及理论分析相结合的方法,对余柏海非线性方程法模拟模型中锰、硼和铬等合金元素的合金化当量公式进行了修正。利用修正后的模型对国内外文献给出的80余种碳素结构钢和单合金结构钢端淬曲线进行了数值模拟,研究表明,模拟曲线与文献给出的曲线吻合的较好。修正后的模型具有较高的可靠性。     

非线性方程法模型中合金元素交互作用因子的确定

采用试错法及数理统计分析,获得了改进非线性方程法模拟模型中合金元素交互作用因子的数值,并给出其适用范围.通过对国内外文献中给出的300多种结构钢端淬曲线模拟效果的比较,表明这两种方法得到的交互作用因子均可满足工程要求,同时现场试验结果也证明了这一点.     

结构钢端淬曲线预测新方法

端淬试样硬度分布模型采用了含有待定参数一淬透性系数的解析函数，本文定义的钢的淬透性系数与现有合金元素的淬透性系数不同，其大小直接决定端淬曲线的递减速率．端淬曲线的计算预测问题转化成淬透性系数的求解．在端淬实验标准状态下，淬透性系数取决于钢的化学成分．用分部逼近法建立了结构钢淬透性系数与化学成分之间的关系式．将计算获得的淬透性系数代入到硬度分布模型对端淬曲线进行了预测，结果表明预测曲线与实验值吻合很好．     

基于人工神经网络的结构钢端淬曲线预测系统模型的研究

利用人工神经网络技术建立了结构钢端淬曲线预测系统的数学模型，该模型覆盖了较大范围的钢种，系统除了能对已训练过的钟种的端淬曲线准确描述外，还能在一定精度范围内对新钢种的端淬曲线进行预测。本文还研究了训练步数、训练数据量对模型预测量精度的影响。     

基于人工神经网络的结构钢端淬曲线预测系统模型的研究

利用人工神经网络技术建立了结构钢端淬曲线预测系统的数学模型,该模型覆盖了较大范围的钢种,系统除了能对已训练过的钢种的端淬曲线准确描述外,还能在一定精度范围内对新钢种的端淬曲线进行预测.本文还研究了训练步数、训练数据量对模型预测精度的影响.     

高铁齿轮用钢18CrNiMo7-6的端淬曲线与模拟计算

采用3种端淬曲线预测模型计算两炉次高铁齿轮用钢18CrNiMo7-6的端淬曲线,对比端淬硬度检测结果,分析了不同模型的预测误差。结果表明,金满模型的预测结果与端淬试验检测结果误差较小,可以用做这类钢的端淬结果预测。根据端淬曲线预测与实测结果的对比分析,结合相关高铁齿轮用钢技术条件的硬度规范对高铁齿轮用钢18CrNiMo7-6的冶炼成分控制给出了建议。     

延性β相稳定性与合金元素合金化行为的研究

基于余氏固体与分子经验电子理论，利用平均原子晶胞模型计算了Ti3Al基合金中延性β相的价电子结构，给出了其价电子结构信息——相结构因子nA，σN，F。用相结构因子nA，σN，F分析讨论了β相价电子结构与稳定性的关系及合金元素的合金化行为。认为β相及含不同合金元素的β相的精细价电子结构是金属间化合物α2-Ti3Al合金化与Ti3Al基合金选择Nb，V，Mo3种合金元素作为常用合金化元素的微观本质原因。     

20CrMnMo钢渗碳后端淬试验的硬度分析

运用端淬曲线模型对渗碳端淬试样的硬度场进行了模拟分析,首先拟合出不同碳含量下的端淬硬度曲线,然后通过温度场的分析得到特定温度区间的冷却曲线,最终实现了渗碳端淬试样硬度的分层模拟和云图显示。通过与试验结果对比,硬度分析结果具有较好的准确性,尤其是在端淬距离较小时,其余位置最大误差在5. 6 HRC以内。     

30XHMA钢中微量碳、氮化物形成元素

前言强碳、氮化物形成元素——钒、钛、铌的微量合金化对钢的组织、机械性能和工艺性能的影响人们曾进行了十分详尽的研究,但是微量合金元素对合金钢可逆回火脆性倾向的影响人们还不完全清楚。为了清除结构钢的回火脆性,通常采用钼(0.5％)来进行合金化。人们注意到在含钼的低磷钢中有减少钼含量可能性的报导,其目的是搞清楚,在用直接还原炉料炼制的纯净钢中,微最碳、氮化物形成元素能在多大程度上来代替钢中钼的作用。本文研究微量合金元素钒(0.05％)、钛     

表面渗铬T8钢中碳迁移的热力学分析

采用双辉等离子渗铬技术，对T8钢进行了900℃温度表面渗铬合金化；使用GDA750．测量了试样渗铬合金层中Fe、Cr和C等元素的浓度分布。通过热力学分析计算，研究了渗铬合金化层形成过程中碳迁移机理和规律。指出驱动碳迁移的动力是碳的括度梯度，并推导出富碳区碳质量分数随铬质量分数变化的数学关系式：w(C）=0．8 0.3536w(Cr)。计算结果与试验结果相当吻合。     

Modeling of Intel-critical Heat Treatment of DP and TRIP Steels

THE characteristic multiphase structure of DP andTRIP steels can be evolved by hot rolling or by postheat treatment.Only this latter case is discussed in thispaper.The transformation processes which result in theexpected microstructure and ratio of constituents arecontrolled by the technological parameters of thetreatment.Although the number of affecting parametersis not so large,the determination of importantparameters'values(intercritical temperature,coolingrate)needs numerous pre-experim…     

Effects of Quench Rate on Mechanical Properties and Microstructures of High-Strength 7046A Aluminum Alloy

JOM - The Jominy end-quench (JEQ) test was adopted to investigate the quench sensitivity of Al-6.6Zn-1.8Mg-0.23Cu-0.22Mn-0.21Zr (7046A) alloy. The mechanical properties of the aged alloy were...     

Modeling of Intercritical Heat Treatment of DP and TRIP Steels

The final microstructure of DP and TRIP assisted steels can evolve after hot working (hot rolling) or during post heat treatment process. In the formation of the final structure a number of different technological parameters have important roles, e.g. end temperature of rolling, cooling rates, temperature of intercritical annealing, etc. As a result of the individual factors and their combinations a lot of product technology routes are feasible. The effect of the different combinations of these technological parameters on the microstructure can be mapped by the special Jominy end-quench test (so called intercritical Jominy end-quench test) described in this paper. Unlike the traditional Jominy test, in this case there is a partial anstenizing between A1 and A3 temperatures which results in a given amount of ferrite in the microstructure before quenching. The method developed can be applied for mapping DP and TRIP assisted steels' microstructure in a wide range of technological parameters. The analysis of measured and calculated data can help us find the technological parameters optimal from the microstructural point of view.     

A numerical simulation of the jominy end-quench test

We present a numerical algorithm for simulating the Jominy end-quench test and deriving continuous cooling diagrams. The underlying mathematical model for the austenite-pearlite phase transition is based on Scheil's Additivity Rule and the Johnson-Mehl equation. For the formation of martensite we compare the Koistinen-Marburger formula with a rate law, which takes into account the irreversibility of this process. We carry out numerical simulations for the plain carbon steels C 1080 and C 100 W 1. The results suggest that the austenite-pearlite phase change may be described decently by the Additivity Rule, except for the incubation time. On the other hand, using a rate law to describe the martensite formation is preferable to the Koistinen-Marburger formula, which leads to unphysical oscillations of the cooling curves in simulated CCT-diagrams.     

Hardenability testing of steels when gas quenching is applied

Abstract

The common test for assessing hardenability is the standardised Jominy end-quench test according to ASTM-A255 or DIN-EN 50191. This test is applied essentially for non-alloyed and low alloyed structural steels, when quenched in liquid quenchants, but it is not applicable for high alloyed (air hardening) steels, because the cooling rate at the opposite end of the Jominy specimen is higher than the critical cooling rate of those steels. Today there is no a standardised method to test and evaluate the hardenability of high alloyed steels. Nowadays, not only high alloyed steels, but also some low alloyed structural steels are quenched by high pressure gas quenching (HPGQ) in vacuum furnaces. Obviously there is need from one side to develop a standardised method for testing and evaluation of hardenability for high alloyed steels when they are gas quenched, and from the other side to establish a database for hardenability of low-alloyed structural steels when they are quenched in vacuum furnaces by HPGQ, i.e. at different high pressures and different flow velocities. Corresponding equipment that can be used to satisfy both requirements is discussed.     

Prediction of jominy hardness profiles of steels using artificial neural networks

Jominy hardness profiles of steels were predicted from chemical composition and austenitizing temperature using an artificial neural network. The neural network was trained using some 4000 examples, covering a wide range of steel compositions. The performance of the neural network is examined as a function of the network architecture, the number of alloying elements, and the number of data sets used for training. A well-trained network predicts the Jominy hardness profile with an average error of about 2 HRC. Special attention was devoted to the effect of boron on hardenability. A network trained using data only from boron steels produced results similar to those of a network trained using all data available. The accuracy of the predictions of the model is compared with that of an analytical model for hardenability and with that of a partial least- squares model using the same set of data.     

描述钢淬透性的一个新数学模型

提出了一个新的描述钢淬透性的数学模型及淬透性表征参数．根据端淬实验数据和实验曲线导数变化规律，用线性试探法建立了端淬曲线微分方程，然后解得硬度分布函数．硬度分布函数将端淬曲线描述为直线段和曲线段构成的分段函数：直线段描述试样端部获得全部马氏体区域的硬度，在此区域硬度保持恒定最高值；在曲线段硬度递减，最后趋近恒定最低值．钢的淬透性值用数学参数来表示，它数值上等于从原点到硬度分布曲线拐点的距离．用非线性模拟程序代入实验值获得了淬透性值．模拟结果表明，所获得的模型与实验值吻合很好．