凝固末端电磁搅拌对大方坯凝固组织的影响

对某钢厂80帘线钢在结晶器和凝固末端复合电磁搅拌(M+F-EMS)条件下生产的连铸大方坯的凝固组织进行了研究,从微观组织、成分偏析等方面进行了分析,探讨了凝固末端电磁搅拌对铸坯凝固组织的影响。结果表明,凝固末端电磁搅拌可以明显的细化80帘线钢铸坯中心部位凝固组织,减少了铸坯中心疏松和缩孔,降低了铸坯中心碳、硫偏析。     

重轨钢大方坯连铸过程中凝固传热和坯壳生长的数值模拟

采用有限元方法,对某钢厂的大方坯连铸机的凝固传热情况以及坯壳凝固生长进行了数值模拟,并通过现场数据对模型进行了验证.以此为依据,对该钢厂新建的重轨钢大方坯在不同的拉速和过热度条件下的凝固过程温度场分布情况、坯壳生长规律和凝固分率进行了预测性的模拟研究,从而为新建的重轨钢大方坯连铸机的结晶器和二冷区冷却制度的制定提供理论依据.     

大方坯连铸凝固末端电磁搅拌的数值模拟和试验分析

通过ANSYS有限元对凝固末端电磁搅拌(F-EMS)进行数值分析,得出凝固末端电磁搅拌的优化工艺参数为电流300 A、频率8 Hz,铸坯中心的磁感应强度能够满足要求.现场冷态测试结果和模拟结果基本吻合.以此为基础为包钢设计并制造出280 mm×380 mm大方坯凝固末端电磁搅拌器.生产试验表明,大方坯凝固末端电磁搅拌能够显著改善铸坯的中心疏松、缩孔等缺陷,减少了碳的偏析.     

中冶东方工程技术有限公司总承包的大方坯连铸机一次热试成功

<正>2018年12月28日,由中冶东方工程技术有限公司为河北永洋特钢集团有限公司设计的R12 m六机六流大方坯连铸机一次热试成功。该连铸机采用蝶式钢包回转台、抛物线管式结晶器和连续矫直曲线以及末端电磁搅拌、自动定重定尺切割等技术;此外,还采用了凝固末端轻压下和动态二冷配水技术,结晶器振动采用了内外弧双单元液压振动,实现非正弦曲线,扇形段采用了辊列控     

特殊钢连铸大方坯末端电磁搅拌位置研究与应用

采用有限元法,建立了大方坯凝固传热过程的数学模型。基于射钉试验验证了该模型的正确性。基于该凝固模型,分析了中心固相率与不同定义下凝固率的关系,提出了中心固相率在0～0.2范围内作为末端电磁搅拌的合理位置,适用于所有钢种;而面积凝固率在0.6～0.8范围内作为末端电磁搅拌的合理位置,只适用于高碳特殊钢。工业性生产试验进一步验证了这一观点,并取得了较好的冶金效果。     

圆坯连铸凝固末端电磁搅拌位置优化

采用射钉法测定了圆坯连铸机的综合凝固系数,利用平方根定律对电磁搅拌器合适的安装位置进行了理论计算。结果表明:该连铸机的综合凝固系数为22~24 mm·min-1/2,末端电磁搅拌器应安装在距离弯月面11.3~12.9 m处。将电磁搅拌器位置从距离弯月面10 m移至12 m后,Φ300 mm铸坯的中心疏松从2.2级降到1.7级,Φ180 mm铸坯的中心疏松从1.5级降到1.4级,铸坯内部质量得到明显改善。     

连铸圆坯凝固末端电磁搅拌位置及工艺参数优化

通过有限元模拟对包钢圆坯铸机连铸过程钢液凝固过程进行分析,确定了凝固末端电磁搅拌的安装位置。采用瞬态磁场分析方法,分析了凝固末端电磁搅拌过程中铸坯内部的磁感应强度、电磁力的分布,并对凝固末端电磁搅拌的工艺参数进行了优化。通过优化计算,在拉速为0.45m/min,比水量为0.18L/kg,过热度为25℃的工艺条件下,430mm铸坯凝固末端的电磁搅拌工艺参数:电流为250A,最佳频率为10Hz。现场对搅拌器内部磁感应强度进行冷态测试,测试结果和模拟结果相符合。     

大方坯连铸机关键技术

论述了大方坯连铸机关键技术,其中包括辊列设计中机型选定,结晶器长度、铸机基本圆弧半径的确定、各矫直点的矫直半径、冶金长度、几何尺寸等的设计及电磁搅拌、结晶器液压振动、二冷水控制、具有动态轻压下功能的拉矫机组等,阐述了大方坯连铸机的国产化情况,为新建大方坯连铸机的技术选配和老的大方坯的连铸机的改造提供了依据。     

大方坯动态轻压下工艺参数的优化对高碳钢内部质量的影响

针对大方坯连铸机生产的高碳钢内部出现缩孔等内部质量缺陷,通过对拉速的凝固末端进行计算,优化轻压下系统确定合适拉速与过热度,进行工业试验,提高内部质量。     

大方坯轴承钢GCr15凝固传热数值模拟

以某钢厂断面尺寸为280 mm×320 mm大方坯轴承钢GCr15为研究对象,借助ProCAST软件,建立了二维大方坯凝固传热模型,研究了拉速、比水量、过热度等工艺参数对铸坯凝固过程的影响,同时通过对铸坯中心固相率的研究,确定了与末端电磁搅拌位置、轻压下区间相匹配的最优拉速。结果表明,拉速的变化对铸坯中心固相率、凝固终点位置的影响最大,比水量的影响较大,过热度的影响最小;拉速每增加0.1 m/min,凝固终点平均增加1.97 m,二冷比水量每增加0.1 L/Kg,凝固终点平均减小0.82 m,过热度每增加10 ℃,凝固终点平均增加0.27 m。最佳拉速为0.85 m/min,此拉速下末端电磁搅拌位置和轻压下区间与铸坯合理的中心固相率相匹配。     

Applicability of Johnson-Mehl-Avrami model to crystallization kinetics of Zr60Al15Ni25 bulk amorphous alloy

The applicability of Johnson-Mehl-Avrami（JMA） model to the crystallization kinetics of Zr60Al15Ni25 bulk amorphous alloy is investigated by differential scanning calorimetry（DSC） under isochronal and isothermal conditions. A criterion Xm, at which a defined function z（x） exhibits the maximum value, is introduced to check the validity of JMA model to the kinetics analysis. The value Of Xm has a constant of 0.632 for JMA model. It is found that the values Of Xm at different isothermal annealing temperatures （743,748, 753 and 758 K） are almost near 0.632, which indicates that the isothermal crystallization kinetics can be modeled by JMA equation. However, the values Of Xm at different heating rates （10, 20, 30 and 40 K/min） are about 0.52, implying that JMA model is not valid to the isochronal crystallization kinetics. The reason why the JMA model is not valid to the isochronal crystallization kinetics is discussed.     

Tests of an Adaptive Network Model for the Identification and Categorization of Continuous-dimension Stimuli

We describe a new adaptive network model, the consequential region model, for the identification and categorization of stimuli varying on multiple, continuous dimensions. If the dimensions are binary-valued, the model reduces to Gluck's (1991; Gluck Gf Bower, 1988a) configural-cue model. The consequential region model attempts to provide an adaptive network mechanism to approximate the computations of the multidimensional-scaling choice model (identification) and the generalized context model (categorization). We begin by describing the architecture of the model and the scheme by which stimuli are represented within it. This scheme is motivated by Shepard's (1987) analysis of stimulus generalization and Gluck's (1991) extension of that analysis to network theories of animal and human learning. The main part of the paper describes five simulation experiments in which we attempted to fit the model to identification and categorization data reported by Nosofsky (1987) and Nosofsky et al. (1989), as well as to some artificial identification data. Nosofsky's (1987) stimuli were Munsell color patches varying in brightness and saturation, which are known to be integral dimensions. Nosofsky et al.'s (1989) stimuli, on the other hand, varied on separable dimensions. The model is able to provide excellent fits to the identification and categorization results, including learning.and transfer data. Our results illustrate how an associative network can show appropriate sensitivity to inter-item similarities among training exemplars as an emergent property of its scheme for representing stimuli.     

Tool wear model based on least squares support vector machines and Kalman filter

In this study, the least squares support vector machines (LS-SVM) and Kalman filter (KF) technique are used to establish the tool wear estimation model. Tool wear prediction model, based on LS-SVM, is given to describe the mapping relationship between input–output factors. The cutting conditions (feed rate, cutting speed, and depth of cut), cutting time, and wear position constitute the input factors and tool wear is the output parameter of the model. In order to improve the accuracy of the LS-SVM results, the KF technique is used to update the tool wear estimated results of LS-SVM-based model, which is called the LS-KF model, according to the measured tool wear values. Experiment work is performed on machining center for cemented carbide ball-end cutter cutting stainless steel. Those two models (LS-SVM model and LS-KF model) are applied to the actual milling machining to verify their performance. Results show that predicted tool wear based on the proposed LS-KF model has more precision than that of LS-SVM model.     

The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel

The purpose of this paper is to develop a predictive model for the prediction of tool flank wear and an optimization model for the determination of optimum cutting conditions in machining 17-4PH stainless steel. The back-propagation neural network (BPN) was used to construct the predictive model. The genetic algorithm (GA) was used in the optimization model. The Taguchi method (TM) was used to find the optimum parameters for both models, respectively. Two steps of experiments have been carried out by machining 6 mm length and 90 mm length of the workpiece, respectively. The experimental scheme was arranged by using an orthogonal array of TM. It has been shown that the predictive model is capable of predicting the tool flank wear in an agreement behavior. The optimization model has also been proved that it is a convenient and efficient method to find the optimum cutting conditions associated with the maximum metal removal rate (MMRR) under different constraints. The constraint is the tool flank wear that can be determined from the predictive model. Furthermore, the systematic procedure to develop the models in this paper can be applied to the usage of the predictive or optimized problems in metal cutting.     

A Hybrid Cutting Force Model for High-speed Milling of Titanium Alloys

In this paper, the Johnson-Cook (JC) strength model is used to describe the flow stress of Ti6AI4V and to estimate two important parameters in Oxley's model: the strain-rate constant and the angle made by the resultant force and the shear plane. The JC model is also incorporated into a finite element method (FEM) simulation for the deformation process of T16AI4V. Finally, a hybrid cutting force model based on the FEM simulation and Oxley's theory is proposed to predict cutting forces when machining Ti6AI4V. Experimental results are found to substantiate the developed model.     

Modeling and experimentation for three-dimensional dynamics of endmills

This paper presents a model for the three-dimensional (3D) dynamic response of endmills while considering the actual fluted cross-sectional geometry and pretwisted shape of the tools. The model is solved using the spectral-Tchebychev (ST) technique. The bending and the coupled torsional-axial behavior of four different fluted endmills is compared to finite element model (FEM) predictions and experimental results obtained using modal testing under free-free boundary conditions. For the first eight modes, including six bending and two torsional/axial modes, the difference between the 3D-ST and experimental natural frequencies is shown to be 3% or less for all four tools tested during this study. For the same modes, the 3D-ST and FEM predictions agree to better than 1%. To demonstrate its application, the 3D-ST model for the fluted section of a commercial endmill is coupled to the spindle-holder to predict the tool-point dynamics using receptance coupling substructure analysis (RCSA) with a flexible connection. The coupled model is validated through experiments.     

基于CS-GMC(1,N)的数控机床全新热误差建模优化方法

为降低机床热误差对数控加工精度的影响,提高灰色模型GMC(1,N)的预测精度,将布谷鸟搜索（CS）算法引入GMC(1,N)灰色模型中,用于优化灰色模型GMC(1,N)的生成系数,构建了基于CS-GMC(1,N)的数控机床热误差预测模型。以小型三轴立式数控铣床为研究对象进行了主轴热误差实验,热误差预测性能分析结果表明：CS-GMC(1,N)模型的预测精度高于PSO-GMC(1,N)模型,为机床主轴热误差建模及后续热误差补偿提供了参考。     

Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals

This paper addresses the weld joint strength monitoring in pulsed metal inert gas welding (PMIGW) process. Response surface methodology is applied to perform welding experiments. A multilayer neural network model has been developed to predict the ultimate tensile stress (UTS) of welded plates. Six process parameters, namely pulse voltage, back-ground voltage, pulse duration, pulse frequency, wire feed rate and the welding speed, and the two measurements, namely root mean square (RMS) values of welding current and voltage, are used as input variables of the model and the UTS of the welded plate is considered as the output variable. Furthermore, output obtained through multiple regression analysis is used to compare with the developed artificial neural network (ANN) model output. It was found that the welding strength predicted by the developed ANN model is better than that based on multiple regression analysis.     

NaOH-NaAl(OH)4-Na2CO3-H2O体系活度因子的计算模型

应用Bromley模型,通过对氢氧化钠、铝酸钠和碳酸钠等溶液体系活度因子的实验数据进行校验与回归分析,获得了各电解质合理的Bromley参数,建立基于Bromley模型的NaOH-NaAl(OH)4-Na2CO3-H2O体系活度因子的计算模型,其适用范围为:质量摩尔浓度分别为m(NaOH)≤8 mol/kg,m(NaAl(OH)4)≤3 mol/kg,m(Na2CO3)≤3 mol/kg且离子强度I≤9 mol/kg.使用该模型和Rard方法计算所得水的活度比较结果表明:该模型正确有效,计算精度较高,各电解质的Bromley参数取值合理;该模型也可用于NaOH-NaAl(OH)4-H2O体系活度因子的计算.     

Numerical model of Ca(OH)2 transport in concrete due to electrical currents

A mathematical model is being developed to describe a repair method in concrete, called cathodic protection (CP). The model is in principle also useful to describe electrodeposition in concrete, e.g. the process of re‐precipitation of Ca(OH)₂ invoked by an electrical current. In CP, the current is sent from an external anode to the reinforcement inside the concrete. This model is implemented using the numerical software package Comsol Multiphysics. The model is based on the Nernst–Planck equations and the electroneutrality condition considering the ionic species Na⁺, OH^? and Ca²⁺ and the solid Ca(OH)₂. The mathematical model makes it possible to predict the location where Ca(OH)₂ precipitates when a certain current density is used. This could be of great use for controlled crack repair in concrete and for electrochemical re‐alkalisation. This paper presents the qualitative behaviour of dissolution and re‐precipitation of Ca(OH)₂ in CP. It discusses model calculations and preliminary experimental results. Experiments for a more complete validation of the model are in process.