用电弧声信号监测GMAW焊丝干伸长的SVM模型

为了探索电弧声在焊接质量监控中的应用途径,在对短路过渡GMAW电弧声信号频谱分析的基础上提出电弧声道概念,认为声道是受焊接参数、电弧形态等众多因素影响的分布参数系统.电弧声的LPC(线性预测)模型是声道传输特性的一个参数化估计.电弧声频谱与焊丝干伸长密切相关,但呈现出高度复杂性和非线性.利用电弧声LPC预测系数和反射系数构造输入向量,建立了支持向量机(SVM)的焊丝干伸长分类模型.训练和测试结果表明,采用不同形式核函数的SVM(支持向量机)分类器均能实现干伸长的正确分类,其性能明显优于相同条件下的RBF(径向基函数)神经网络分类模型,小样本情况下仍具有较好的推广能力.其中,用反射系数作为输入向量训练三次多项式核函数的SVM分类器性能最优,测试正确率在98%以上.据此认为,利用LPC分析提取电弧声的特征向量,建立SVM模型是一种焊接动态参数监控的可行方法.     

摩擦焊接轴向缩短量神经网络与支持向量机预测

轴向缩短量是惯性摩擦焊接过程中的关键参量.文中利用ABAQUS有限元软件对高温合金管材惯性摩擦焊接过程进行了模拟,获得并研究了不同焊接工艺参数下的轴向缩短量结果.基于模拟结果,分别建立了支持向量机(SVM)和径向基函数(RBF)神经网络的轴向缩短量的预测模型.两种模型的对比表明,对于该小样本的预测,RBF神经网络比SVM智能预测结果更接近有限元模拟值.因此RBF神经网络模型可以更好的辅助摩擦焊接的有限元模拟,并有效降低模拟的时间成本.     

盘型激光焊接状态多传感信息融合分析

针对大功率盘型激光焊接状态,研究一种基于支持向量机的多传感信息融合分析方法. 使用紫外、可视和红外波段的两个高速摄像机同时获取激光焊接过程中金属蒸气、飞溅和熔池动态图像. 通过模式识别技术提取焊接过程多传感信息特征及进行数据主成分特征分析,并以焊缝宽度变化作为衡量焊接状态稳定性的参数. 运用支持向量机融合各特征,通过网格搜索和粒子群算法优化支持向量机参数,建立基于支持向量机的多传感信息融合模型. 结果表明,支持向量机多传感信息融合方法能够有效预测焊缝宽度变化趋势,为大功率盘型激光焊接状态的实时监控提供试验依据.     

焊接成形中焊接参数的选取与焊缝尺寸的预测

通过TIG堆焊试验,获取了在一定焊接参数下成形焊缝的熔宽、余高尺寸数据.建立了双BP神经网络模型:模型1,由焊接参数预测焊缝尺寸;模型2,由焊缝尺寸预测焊接参数.可先通过模型2预测一定尺寸的焊缝所对应的焊接参教,对预测的焊接参数作适当调整后再输入到模型1中,检验该焊接参数条件下获得的焊缝尺寸是否符合要求.可以利用此双模型选取合适的焊接参数来对焊缝尺寸进行控制,取得了较好的试验效果.     

激光焊熔池红外辐射特征支持向量机回归分析

大功率光纤激光焊接过程中,熔池红外辐射蕴含着丰富的焊接质量信息。以大功率光纤激光对接焊304不锈钢板为试验对象,运用近红外高速摄像机获取焊接熔池动态热像。定义并提取熔池宽度、匙孔面积、匙孔周长和匙孔质心横、纵坐标,作为熔池特征参数,运用支持向量机建立熔池特征参数和焊缝宽度的回归模型,并通过网格寻优和粒子群算法优化支持向量机参数。试验表明,所建立的支持向量回归机能够较好地融合熔池特征,预测焊缝宽度,从而为自动监控大功率光纤激光焊接质量提供试验依据。     

焊缝偏差RBF神经网络预测算法

以10 kW大功率光纤激光焊接304奥氏体不锈钢板为试验对象,研究一种焊缝偏差预测算法.利用红外摄像机摄取焊接过程中的熔池红外图像,提取匙孔质心、匙孔形状参数和热堆积效应参数等反映激光束与焊缝位置偏差的特征量作为径向基函数RBF神经网络预测模型的输入量,建立焊缝偏差RBF神经网络预测模型.选择焊缝偏差特征量作为训练样本并对预测模型进行训练,建立焊缝偏差预测模型.结果表明,该模型能够对大功率光纤激光焊接过程中的激光束与焊缝位置之间的偏差进行有效预测.     

基于改进型支持向量机算法的轧机轧制力预测

考虑到基于神经网络算法建立的预测模型虽然具有较好的预测精度,但是神经网络模型需要大量的训练样本,另外会增加模型的复杂程度,研究了一种基于改进型支持向量机的轧机轧制力预测模型,建立基于RBF核函数和多项式核函数的最小二乘支持向量机,并使用协同量子粒子群算法对混合函数的参数进行寻优,以提高预测模型的预测性能。由协同量子粒子群算法优化得到了基于改进型支持向量机的轧机轧制力预测模型中的RBF核函数参数γ值、惩罚系数c值、多项式核函数参数q值和两个核函数的权重a值。通过实例研究表明:使用本文研究的改进型支持向量机的轧制力预测模型预测相对误差在4%～6%之间,多组数据的平均值误差为4. 83%。验证了本文研究的基于改进型支持向量机的轧机轧制力预测模型的可行性。本文研究的预测模型相比其他3种对比模型耗时更长,但是相比之下提高了预测准确率,更具有实际意义。     

基于组合模型的MAG焊工艺参数多目标优化

以MAG焊焊接电压、焊接速度、送丝速度为可调工艺参数，开展了三因素三水平全因子平板对接焊和堆焊试验.基于试验数据建立了误差反向传播神经网络、径向基神经网络和克里金模型来预测焊缝余高、接头抗拉强度和冲击吸收能量.模型预测结果显示，所建立模型均能较好的预测焊缝性能，但是没有一个模型能同时最佳预测三种焊缝性能且各模型预测波动较大. 为了进一步提升预测精度和稳定性，将误差反向传播神经网络、径向基神经网络和克里金模型以线性加权法组合. 结果表明，组合模型能提升预测的精度和稳定性.基于组合模型，采用NSGA-II算法实现多目标优化，得到并验证了焊缝余高、接头冲击吸收能量和抗拉强度三者间的非劣解.验证结果表明焊接工艺多目标优化对实现焊缝综合性能整体最优以及焊接精细化应用具有较大的指导意义.     

基于支持向量机的CO2焊接保护气流量识别研究

利用线性预测(LPC)分析方法建立电弧声信号数学模型并提取声音频域特征,分别用电弧声的LPC预测系数及反射系数作为输入向量构造样本集,建立CO2焊接过程支持向量机模型,对焊接过程保护气体流量进行了识别和分类.测试表明:利用电弧声建立的SVM模型能对焊接气流量进行有效识别.     

基于响应面法的超高频电弧增材制造工艺优化

采用响应面法对超高频电弧增材制造工艺（UHFP-GTAW）进行了优化. 通过建立函数关系得到了焊缝尺寸关于焊接电流、送丝速度及焊接速度的多元二次回归模型，并运用方差分析验证了回归模型的可靠性. 设置优化条件选取了相应的焊接参数，并将焊缝尺寸的预测结果与试验结果进行了对比.结果表明，模型预测尺寸与实际试验测得的焊缝尺寸误差分别为焊缝宽度5.4%和焊层高度6.6%. 对沉积成形的GH4169高温合金薄壁构件分别进行了水平方向和垂直方向的拉伸力学性能测试，其强度极限分别达到1 130.93和1 126.04 MPa，断后伸长率分别为18.1%和16.56%，断面收缩率分别达到20.6%和20.2%.     

Artificial neural network approach for estimating weld bead width and depth of penetration from infrared thermal image of weld pool

Abstract

In this article an artificial neural network based system to predict weld bead geometry using features derived from the infrared thermal video of a welding process is proposed. The multilayer perceptron and radial basis function networks are used in the prediction model and an online feature selection technique prioritises the features used in the prediction model. The efficacy of the system is demonstrated with a number of welding experiments and using the leave one out cross-validation experiments.     

Modelling weld bead geometry using neural networks for GTAW of austenitic stainless steel

Abstract

The authors analyse the importance of different weld control parameters on the weld pool geometry of gas tungsten arc welding using an online feature selection technique that suggests weld voltage and vertex–angle pair as more important than the weld voltage and torch speed pair. Using the selected features multi layer perception and radial basis function networks are developed for prediction of bead width, penetration depth, and bead area. With cross-validation the authors have extensively studied the performance of composite models (one model for all outputs) and individual models (one model for each output). The individual models are found to work better than composite models. Usually, radial basis function networks are found to work better than the multi layer perception networks. To assess the influence of weld control parameters the authors have studied the performance of both networks using different combination of inputs. Overall, the performance of the proposed models is found to be quite satisfactory.     

基于RBF网络的P-GMAW焊缝成形过程建模

分析了薄板平板堆焊熔透的影响因素,利用径向基函数网络（RBFN）,建立了脉冲熔化极气体保护焊（P-GMAW）的焊缝成形过程模型.结果表明,在P-GMAW薄板堆焊中,以焊接规范为输入变量,利用该网络模型能很好的预测背面熔宽.     

Application of RVM for prediction of bead shape in underwater rotating arc welding

Bead shape in underwater rotating arc welding was affected by several welding parameters.RVM(relevance vector machine)was used to build a model to predict weld bead shape.The training data set of RVM consists of the welding parameters which are rotational frequency,rotational radius,height of torch and welding current and the features of the bead shape.The maximum error and mean error for prediction of width are 0.10 mm and 0.09 mm,respectively,and the maximum error and mean error for prediction of penetration are 0.31 nun and 0.12 mm,respectively,which are showed that the prediction model can achieve higher prediction precision at reasonably small size of training data set.     

Sensitivity analysis of submerged arc welding process parameters

Selection of process parameters has great influence on the quality of a welded connection. Mathematical modelling can be utilized in the optimization and control procedure of parameters. Rather than the well-known effects of main process parameters, this study focuses on the sensitivity analysis of parameters and fine tuning requirements of the parameters for optimum weld bead geometry. Changeable process parameters such as welding current, welding voltage and welding speed are used as design variables. The objective function is formed using width, height and penetration of the weld bead. Experimental part of the study is based on three level factorial design of three process parameters. In order to investigate the effects of input (process) parameters on output parameters, which determine the weld bead geometry, a mathematical model is constructed by using multiple curvilinear regression analysis. After carrying out a sensitivity analysis using developed empirical equations, relative effects of input parameters on output parameters are obtained. Effects of all three design parameters on the bead width and bead height show that even small changes in these parameters play an important role in the quality of welding operation. The results also reveal that the penetration is almost non-sensitive to the variations in voltage and speed.     

Fuzzy Regression Model to Predict the Bead Geometry in the Robotic Welding Process

Recently, there has been a rapid development in computer technology, which has in turn led to develop the fully robotic welding system using artificial intelligence (AI) technology. However, the robotic welding system has not been achieved due to difficulties of the mathematical model and sensor technologies. The possibilities of the fuzzy regression method to predict the bead geometry, such as bead width, bead height, bead penetration and bead area in the robotic GMA (gas metal arc) welding process is presented. The approach, a well-known method to deal with the problems with a high degree of fuzziness, is used to build the relationship between four process variables and the four quality characteristics, respectively. Using these models, the proper prediction of the process variables for obtaining the optimal bead geometry can be determined.     

Sensitivity analysis for process parameters in GMA welding processes using a factorial design method

Generally, the quality of a weld joint is strongly influenced by process parameters during the welding process. In order to achieve high quality welds, mathematical models that can predict the bead geometry and shape to accomplish the desired mechanical properties of the weldment should be developed. This paper focuses on the development of mathematical models for the selection of process parameters and the prediction of bead geometry (bead width, bead height and penetration) in robotic GMA (Gas Metal Arc) welding. Factorial design can be employed as a guide for optimization of process parameters. Three factors were incorporated into the factorial model: arc current, welding voltage and welding speed. A sensitivity analysis has been conducted and compared the relative impact of three process parameters on bead geometry in order to verify the measurement errors on the values of the uncertainty in estimated parameters. The results obtained show that developed mathematical models can be applied to estimate the effectiveness of process parameters for a given bead geometry, and a change of process parameters affects the bead width and bead height more strongly than penetration relatively.     

Thermal analysis of horizontal fillet joints by considering bead shape in gas metal arc welding

Abstract

In gas metal arc (GMA) welding, the weld size, that is, the locally melted area of a workpiece, is one of the most important factors determining the strength of a welded structure. Variations in the welding power and the welding heat flux may affect the weld pool formation and ultimately the size of the weld. Therefore, an accurate prediction of the weld size requires a precise analysis of the weld thermal cycle. In the present study, a model that can estimate the weld bead geometry and a method for thermal analysis, including the model, are suggested. To analyse the weld bead geometry, a mathematical model was developed with transformed coordinates to apply to horizontal fillet joints. A heat flow analysis was performed using a two-dimensional finite element model that was adopted to compute the base metal melting zone. The reliability of the proposed model and the thermal analysis was evaluated through experiments, and the results showed that the proposed model was highly effective in predicting the weld bead shape, and that the predicted melting zone of the base metal also corresponded well with the experimental profile.     

空间位置脉冲MAG焊焊缝成形的回归模型

通过正交试验设计和统计学的方法建立了空间位置脉冲ＭＡＧ焊焊缝成形的回归数学模型，该模型较准确地刻划了焊接工艺参数，焊缝空间位置姿态以及焊缝形状尺寸与焊缝成形的关系，该数学模型可用于焊疑成形质量的监测与控制，以可用于焊接规范的优化设计。