GH99合金TIG焊接接头拉伸性能的人工神经网络预测

利用Matlab7.0软件建立了用于预测GH99高温合金焊接接头拉伸性能的改进算法的多层BP神经网络.以焊接电流、焊接速度、脉冲频率、重熔次数、板厚、装配间隙、坡口与连接形式作为输入参数,抗拉强度、屈服强度和断后伸长率分别作为输出值.结果表明,改进算法的多层BP神经网络能够很好的预测GH99高温合金TIG焊接接头的拉伸性能,抗拉强度、屈服强度与断后伸长率预报值与试验值的平均相对误差分别为-0.76%,1.71%和2.30%.     

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.     

CuNiCrSi铜合金搅拌摩擦焊工艺参数与组织性能分析

为了探究CuNiCrSi铜合金搅拌摩擦焊工艺参数与组织性能，采用正交试验法对工艺参数进行优化，并研究了焊缝的组织性能及材料流动性. 结果表明，焊接速度是对焊接过程影响最为显著的因素，试验得到焊接最佳工艺参数组合，在最优参数组合下焊缝的抗拉强度为491.4 MPa，达到母材抗拉强度的84.7%. 当焊接速度过大时会在焊缝的前进侧出现隧道型缺陷；材料的流动可以分为四部分；前进侧材料流动情况比返回侧复杂；焊接速度过小时表面会出现毛刺；母材区粗大的粒子为铬和硅的偏聚物，而镍无偏聚均匀地分布在材料中；焊核区晶粒随着焊接速度的增加而变小. 最优参数下焊核区晶粒虽然细小均匀，但硬度却比母材区低.     

PMMA与304不锈钢激光焊接

研究聚甲基丙烯酸甲酯(PMMA)塑料与304不锈钢激光焊接热传导技术,采用正交试验法分析聚甲基丙烯酸甲酯和304不锈钢之间的焊接质量. 对焊接后的试样进行拉伸测试和切片试验,用能量密度定量判定焊接结果,分析多种焊接因素对抗拉强度和焊缝宽度的影响. 利用正交试验极差分析法对试验数据进行处理,获得透明聚甲基丙烯酸甲酯和304不锈钢之间激光热传导焊接的最佳焊接工艺参数. 结果表明,焊接因素对焊接强度的影响从大到小的顺序为焊接速度、脉冲宽度、保护气体流量、峰值功率、光斑直径和脉冲频率.     

基于正交试验-BP神经网络的GH4169膜片微束TIG焊接工艺优化

将正交试验与BP神经网络结合用于0.2 mm厚GH4169膜片微束TIG焊接工艺参数优化,根据正交试验结果对神经网络模型进行训练,建立了峰值电流、基值电流、焊接速度、脉冲频率与接头直径、高度、抗拉力的BP神经网络模型. 结果表明,在BP神经网络模型预测的基础上,结合小步长搜索法获得的最佳工艺参数范围为峰值电流11.6 A±0.2 A、基值电流4.3 A±0.1 A、焊接速度4.14 mm/s±0.1 mm/s、脉冲频率52 Hz±2 Hz. 通过试验验证,4组试样各指标试验值均处于模型预测值范围内,抗拉力值均高于先期试验. 试验结果证明,该模型预测精度高,并且该工艺优化方法能有效提高实际工艺设计的效率.     

6061-T6铝合金微搅拌摩擦焊工艺

以0.8 mm厚6061铝合金微搅拌摩擦焊对接过程为研究对象,采用专用搅拌工具,通过温度场模拟进行工艺参数预选,研究了无倾角微搅拌摩擦焊的工艺参数对接头力学性能的影响,确定了与所设计微搅拌工具相匹配的工艺参数窗口;并采用光学显微镜、SEM扫描电镜对接头的微观组织、断口的形貌进行观察. 结果表明,在焊接速度为300 mm/min、转速14 000 ~ 24 000 r/min时,可以获得力学性能优越的焊接接头,抗拉强度均可达母材的70%以上;微搅拌摩擦焊缝微观组织的热影响区与传统搅拌摩擦焊相比,仅部分晶粒发生长大,仍有部分晶粒与基体保持一致无明显变化.     

基于神经网络的搅拌摩擦焊接头质量预测系统研究

通过正交试验在不同的试验参数下对铜-铜进行搅拌摩擦焊接得到覆盖面较广的试验数据．形成神经网络质量预测体系的训练样本。以搅拌摩擦焊的工艺参数(旋转速度、横向速度、压力)作为神经网络的输入，以焊接接头的拉仲强度或接头优异性(1接头质量合格、O为接头质量不合格)为神经网络的输出，用剔除畸变样本后的试验样本对人工神经网络进行训练，然后用训练好的网络对搅拌摩擦焊接头质量进行预测。利用MATLAB语言编制用户界面，并建立了搅拌摩擦焊的人工神经网络预测系统，实现了对工艺参数的优化。     

铝合金无减薄搅拌摩擦焊工艺优化及特征分析

为提高无减薄搅拌摩擦焊接头力学性能,基于响应面法对参数进行优化,建立了响应面模型,并对模型进行回归分析. 结果表明,无减薄搅拌摩擦焊是成形优良的焊接工艺,而且焊接参数对接头拉伸性能影响明显,其中主轴转速及焊接速度对其影响更为显著. 在无缺陷条件下,提高主轴转速的同时选取适中的焊接速度,以得到性能更优的焊接接头. 焊接参数为主轴转速1 000 r/min,焊接速度200 mm/min、轴肩下压量0.25 mm时,接头的抗拉强度最大为363 MPa,为母材的94.3%,断后伸长率11.2%. 而且相比于常规搅拌摩擦焊,无减薄搅拌摩擦焊在厚度方向上的性能更加均匀.     

Optimization of friction welding parameters using evolutionary computational techniques

The purpose of this study is to propose a method to decide near optimal settings of the welding process parameters in friction welding of stainless steel (AISI 304) by using non conventional techniques and artificial neural network (ANN). The methods suggested in this study were used to determine the welding process parameters by which the desired tensile strength and minimized metal loss were obtained in friction welding. This study describes how to obtain near optimal welding conditions over a wide search space by conducting relatively a smaller number of experiments. The optimized values obtained through these evolutionary computational techniques were compared with experimental results. The strength and microstructural aspects of the processed joints were also analyzed to validate the optimization.     

304不锈钢薄板列置双TIG高速焊缝组织与性能

相比常规速度不锈钢焊接,高速焊接过程中焊缝金属的凝固过程及组织形态将会发生变化,从而影响焊缝组织和性能. 对1.2 mm厚304不锈钢薄板对接,采用列置双TIG焊在焊接速度为3.0 m/min时获得了良好焊缝成形,并与常规速度单TIG焊接工艺相比,采用非标准拉伸试样测试了焊缝性能,并分析了其组织. 结果表明,高速双TIG焊接焊缝中心生成等轴晶,两侧树枝晶未形成对向生长的定向晶粒,焊缝抗拉强度及断后伸长率略低于母材;相比常规单TIG焊接工艺,高速双TIG焊接热影响区晶粒平均直径降低了10.3%,但焊缝中心晶粒平均直径增大了12.9%;焊缝抗拉强度和断后伸长率分别提高了4.3%和23.2%,焊缝中心硬度值略高于母材.     

铝合金振动焊接过程非线性时间序列模型分析

针对7系列超硬铝在传统熔焊过程中易出现热裂纹、气孔和焊接接头软化等问题,研究振动焊接工艺过程中,焊接工艺参数变化与焊接接头强度间的非线性关系机理,建立基于焊接过程工艺参数测量数据的7075超硬铝振动焊接接头强度非线性时间序列预测模型. 文中在7075超硬铝振动焊接过程参数测量数据的基础上,建立了焊接过程参数时间序列,并在此基础上研究建立了焊接过程系统相空间重构参数及确定性检验方法. 根据重构相空间的相点演化轨迹与焊接接头强度参数间的非线性关系,建立相空间相点演化轨迹的人工神经网络拟合模型,对焊接接头的断后伸长率、抗拉强度、硬度、焊缝余高、晶枝最大粗度、晶粒数量等物理参数进行计算. 根据建立的模型进行的一系列焊接接头强度试验显示. 结果表明,该模型的预测结果可以满足工程需要,具有工程实用价值.     

Prediction of Tensile Property of Hydrogenated Ti600 Titanium Alloy Using Artificial Neural Network

An artificial neural network (ANN) model has been developed to analyze and predict the correlation between tensile property and hydrogenation temperature and hydrogen content of hydrogenated Ti600 titanium alloy. The input parameters of the neural network model are hydrogenation temperature and hydrogen content. The output is ultimate tensile strength. The accuracy of ANN model was tested by the testing data samples. The prediction capability of ANN model was compared with the multiple linear regression approach and response surface method. The combined influence of inputs on the tensile property is also simulated using ANN model. It is found that excellent performance of the ANN model was achieved, and the results showed good agreement with experimental data. Moreover, the developed ANN model can be used as a tool to control the tensile property of titanium alloys.     

基于响应面法7A52高强铝合金FSW接头抗拉强度预测及优化

为了研究7A52铝合金搅拌摩擦焊的焊接速度、搅拌头转速及轴肩压深对接头抗拉强度的影响,采用响应面法的中心复合试验设计法设计20组试验,并建立抗拉强度响应函数关系式. 为了验证响应函数关系式的精确性,通过方差分析和回归分析确定该回归模型为显性,相关性系数R2的偏差为3.17%. 通过单一焊接参数因素和双因素焊接参数对抗拉强度的影响分析,进一步验证了模型的准确性,最后通过拉伸试验验证. 结果表明,基于响应面法拟合的搅拌摩擦焊焊接速度、搅拌头转速及轴肩压深与接头抗拉强度响应函数关系式能精确的预算不同焊接参数组合所对应的接头抗拉强度,并获得接头最佳参数组合为焊接速度110 mm/min、搅拌头转速1 436 r/min和轴肩压深0.55 mm,得到最大预测抗拉强度为380 MPa.     

低活化马氏体钢真空扩散焊接头力学性能

在不同焊接工艺参数下对低活化马氏体钢进行真空扩散焊接试验,分别对焊缝区进行光学显微观察(OM)与扫描电镜观察(SEM)分析,并对焊件进行力学性能测试. 结果表明,低活化马氏体钢的焊后组织主要为板条马氏体及少量的残余奥氏体,焊缝结合状况良好;在一定范围内提高焊接温度、延长保温时间可以提升接头抗拉强度,但过高的温度及保温时间会导致奥氏体晶粒粗化,损害接头的抗拉强度及冲击韧性. 经过焊后热处理(正火+回火)的接头抗拉强度相对于热处理前有所降低,但组织稳定性及冲击韧性有一定改善.     

Weldability evaluation and tensile strength estimation model for aluminum alloy lap joint welding using hybrid system with laser and scanner head

Aluminum welding using a hybrid system with a laser and scanner welding head was performed under various welding conditions to verify the feasibility of applying an aluminum alloy to a car body.The experimental material was 5J32 aluminum alloy,and the laser power,welding speed,and laser incidence angle were used as the control variables.The weld bead shape and the tensile shear strength were evaluated in order to understand the aluminum lap joint weld characteristics.Analysis of variance (ANOVA) was conducted to identify the effect of the process variables on the tensile shear strength.Tensile strength estimation models using three different regression models were also suggested.The input variables were the laser power,welding speed,and laser incidence angle,and the output was the tensile shear strength.Among the models,the second-order polynomial estimation model had the best estimation performance,and the average error rate of this model was 0.058.     

Optimizing friction stir welding parameters to maximize tensile strength of AA2219 aluminum alloy joints

AA2219 aluminium alloy (Al-Cu-Mn alloy) has gathered wide acceptance in the fabrication of lightweight structures requiring a high strength-to-weight ratio and good corrosion resistance. In contrast to the fusion welding processes that are routinely used for joining structural aluminium alloys, the friction stir welding (FSW) process is an emerging solid state joining process in which the material that is being welded does not melt and recast. This process uses a non-consumable tool to generate frictional heat in the abutting surfaces. The welding parameters such as tool rotational speed, welding speed, axial force etc., and the tool pin profile play a major role in determining the joint strength. An attempt has been made here to develop a mathematical model to predict the tensile strength of friction stir welded AA2219 aluminium alloy by incorporating FSW process parameters. A central composite design with four factors and five levels has been used to minimize the number of experimental conditions. The response surface method (RSM) has been used to develop the model. The developed mathematical model has been optimized using the Hooke and Jeeves search technique to maximize the tensile strength of the friction stir welded AA2219 aluminium alloy joints.     

A Comparative Study of Artificial Neural Network and Response Surface Methodology for Optimization of Friction Welding of Incoloy 800 H

This article deals with the optimization of process parameters for friction welding of Incoloy 800 H rod and compares the results obtained by response surface methodology(RSM) and artificial neural network(ANN).The experiments were carried out on the basis of a five-level,four-variable central composite design.The output parameters were the tensile strength and burn-off length(BOL).They were considered as a function of four independent input variables,namely heating pressure(HP),heating time,upsetting pressure(UP),and upsetting time.The RSM results showed that the quadratic polynomial model depicted the interconnection between individual element and response.For optimizing the process parameters,ANN analysis was used,and the optimal configuration of the ANN model was found to be 4–9–2.For modeling aspect,a requisite trained multilayer perceptron neural network was rooted,and a quick propagation training algorithm was used to train ANN.The purpose of optimization was to decide the maximum tensile strength and minimum burn-off length of the welded joint which was done by varying the friction welding process variables.The order of importance of input parameters for friction welding of Incoloy 800 H was HP [ UP [ N [ BOL.After predicting the model using RSM and ANN,a comparison was made for predicting the effectiveness of two methodologies.By analyzing the results,it was observed that as compared to RSM,ANN model was more specific.     

W6高速钢/16Mn钢电子束焊接接头组织及性能分析

高速钢作为一种高硬度、高耐磨性和高耐热性特殊工具钢,应用于刀具、模具及特殊结构件上时,往往需要结合异种钢使用. 但高速钢焊接工艺研究仍不成熟,焊接中产生的裂纹与碳化物缺陷是制约高速钢应用的主要因素. 文中通过对W6Mo5Cr4V2高速钢与16Mn钢预置镍填充层后进行电子束焊接. 结果表明,镍中间层的引入有效的抑制了高速钢侧热影响区的开裂,接头呈不对称“漏斗形”. 焊缝组织主要由镍基固溶体与少量M₂C碳化物构成,焊缝中无马氏体组织,其焊缝平均硬度为185 HV;接头抗拉强度达到378 MPa,为16Mn侧母材抗拉强度的75%. 拉伸断口断裂于距W6侧熔合线0.8 mm处的热影响区,为准解理断裂.     

Inconel 718镍基合金与304不锈钢电子束焊接

对Inconel 718镍基合金与304不锈钢进行了电子束焊接试验,分析了接头显微组织及力学性能. 结果表明,焊缝区中部由枝晶及细小的等轴晶组成,在近镍侧及近钢的熔合线,都由向焊缝中心方向生长的树枝晶组成. 各特征区域显微硬度值各不相同,焊缝区高于镍基合金侧,高于不锈钢侧. 当焊接束流为8 mA,焊接速度为700 mm/min时,接头的抗拉强度最高为722 MPa. 拉伸试样断裂发生于焊缝区内部,呈典型的延性断裂,断口可观察到明显等轴状韧窝.     

基于径向基神经网络焊接接头力学性能预测

利用径向基神经网络建立了TC4钛合金,TIG焊焊接工艺参数与接头力学性能关系的网络模型.训练模型使用了27组数据,并对另外9组数据进行仿真.结果表明,以焊接电流、焊接速度和氩气流量作为网络输入参数,利用所建的模型能够对该焊接接头抗拉强度、抗弯强度和断后伸长率进行较为准确的预测.通过与常用的标准BP神经网络模型比较发现,径向基网络相对于BP网络预测精度有了大幅度的提高,克服了BP网络训练时间长和容易陷入局部极小的缺点,为实现焊接接头力学性能预测提供了一条有效途径.