GMAW焊接熔滴长大和脱离动态过程的数学分析

考虑GMAW焊接过程中熔滴过渡的主要特点,利用“质量-弹簧”理论建立了熔滴过渡的数学模型,对连续电流条件下GMAW焊接熔滴过渡过程进行了动态力学分析。定量分析了熔滴过渡形式、振荡速度等对熔滴过渡行为的影响,预测出了不同焊接电流条件下的熔滴脱离尺寸和过渡频率。结果表明,在同一焊接电流条件下, 上一个熔滴的脱离会影响到下一个熔滴在焊丝末端的振荡,并影响熔滴的尺寸和过渡周期:熔滴尺寸和过渡频率的理论计算值与试验数据基本吻合。     

Effect of forces on dynamic metal transfer behavior of cable-type welding wire gas metal arc welding

A cable-type welding wire (CWW) gas metal arc welding (GMAW) method was proposed as a novel approach, using CWW for the consumable electrode. Droplet transfer influences the welding process, and the forces on the droplet were analyzed to elucidate the metal transfer phenomenon observed during the welding process. The effects of the arc pressure, rotating force, and welding parameters were analyzed to understand the metal transfer. The special structure of the CWW affected the arc characteristics and forces during metal transfer as part of the welding process. The droplet formed by droplets from each thin wire, the arc, and electromagnetic forces on droplet formation and the coupling process were analyzed. The arc pressure and rotating forces are beneficial to metal transfer and increase the droplet transfer frequency. The droplet size decreases with increasing welding parameters.     

Monitoring of liquid droplets in laser-enhanced GMAW

In the laser-enhanced gas metal arc welding (GMAW) process developed recently, droplets of melted metal can be detached from the wire under relatively low currents with the assistance of an auxiliary force provided by a laser. The stability of the arc and the quality of the resultant welds are improved. To compete with the gas tungsten arc welding of the much lower productivity in joining precision, the size of the droplet can be pre-defined and be controlled to meet the requirements from different applications. For this purpose, image processing algorithms are developed to measure the size of a growing droplet during the laser-enhanced GMAW process. The relatively low contrast, strong illumination and reflection caused by the laser, and strong radiation from the arc make an automatic processing of the image challenging. Images are analyzed to understand its characteristics and design the image processing and recognition algorithms accordingly. In particular, a model-based method is used to filter out non-droplet edge points and a second order equation in the polar coordinate system is introduced to model the droplet. Experimental results verified the effectiveness of the developed algorithms.     

Impacts of external longitudinal magnetic field on arc plasma and droplet during short-circuit GMAW

In gas metal arc welding, electromagnetic force, plasma stream force, gravity, and surface tension are the most important factors that affect metal transfer and spatter generation rate. In this paper, different kinds of external electromagnetic fields were introduced to gas metal arc welding (GMAW). The photos of arc plasma and droplet and electric signals covering welding current and arc voltage were acquired synchronously by an analysis and evaluation system based on LabView for GMAW. It was confirmed that the metal transfer frequency was improved, and spatter generation rate was diminished under controls of external electromagnetic fields. The influencing rules of external electromagnetic fields on electromagnetic force, the gravity, the plasma stream force, and surface tension were studied by three physical models, and the mechanism of external electromagnetic fields was revealed. This paper is for the purpose of discussing these factors and will make a profit for the application of electromagnetic coupling control to short-circuit GMAW.     

Investigation on droplet momentum in VPPA-GMAW hybrid welding of aluminum alloys

Variable polarity plasma arc-gas metal arc welding (VPPA-GMAW) is a superior technology for welding thick plates of high-strength aluminum alloys. It integrates the advantages of energy focusing and high penetration depth in VPPA welding, and those of high welding efficiency and wide range of technological parameters in GMAW process. In this work, we investigated the droplet momentum in paraxial VPPA-GMAW hybrid welding of 7A52 aluminum alloys, and the technological parameters of welding process was also optimized. The images of droplet transfer were captured by high-speed camera, while the droplet speeds and sizes were statistically analyzed by t tests of independent samples. The results showed that the speeds of droplet arriving at the weld pool were significantly between GMAW and VPPA-GMAW processes, and the droplet speed increases with increasing plasma currents within a certain range. Meanwhile, the droplet momentum in VPPA-GMAW process is larger than that in conventional GMAW process. We also found that as the droplet momentum increased, the depression of weld pool grew more obvious and greatly facilitated the deep-penetration welding. In VPPA-GMAW process, it became more and more easier for the droplet to fall off the wire when the electromagnetic force gradually increased during pulse period. Droplet movement through the arc zone was further accelerated since the central pressure of arc column increased during base period. This research can provide some theoretical support for thick plate welding of high-strength aluminum alloys and help for deeper understanding of the hybrid arc coupling mechanism.     

CO2气体保护焊短路过渡与电弧电压关系的研究

针对CO2短路过渡气体保护焊过程中的飞溅问题，通过焊接质量分析仪采集电压、电流动态信息，找到与飞溅相关的熔滴缩颈、熔滴短路和熔滴小桥爆断时刻的标志信息，为短路过渡气保焊过程在线监控提供高品质的信息源。     

Experimental Study on Metal Transfer and Welding Spatter Characteristics of Cellulose Electrode

Welding spatter cause many problems during the welding process and this issue is particularly important for cellulose electrode welding.The hot flying spatter balls often deteriorate the working environment,and decrease the welding efficiency.Many factors affect the welding spatter,and metal transfer behavior is one of the main factors.Many studies concerning the spatter mechanism in arc welding process were made;most of them focused on the solid wire welding and the study on cellulose electrode is rarely reported.In this paper the metal transfer behavior and the weld spatter characteristics of three commercial cellulose electrodes were studied experimentally by using a high speed camera for visually capturing the metal transfer.The relationship between the metal transfer and the welding spatter was analyzed experimentally by comparing the spatter loss coefficient,which is for quantitative evaluation of welding spatter,with the statistical analysis of the large droplet transfer mode.The results showed that short circuiting transfer,large droplet spray transfer,fine droplet spray transfer and explosive transfer govern the metal transfer modes in cellulose electrode welding.Weld spatter occurred mainly in the deflection of large droplet process,explosive transfer process and fine droplet spraying process.Different metal transfer modes lead to different spatter.The deflection of large droplet and explosive transfer are the main factors of the spatter formation.Minimizing the droplet size and reducing the deflection of large droplet and explosive transfer leads to the reduction the amount of spatter in cellulose electrode welding.     

高强度X80钢的GMAW熔滴过渡行为研究

高强度X80钢管道的主线焊接方式为GMAW,根焊时采用内部短弧控制.本文研究了X80钢的GMAW熔滴过渡行为,采用高速摄影设备,拍摄观察了熔滴过渡过程,并提取了短路过渡中对应的电信号,研究了它们之间的动态响应过程.研究发现,熔滴过渡过程中受电磁力的影响较大,合适的焊接参数可以使熔滴平稳过渡.     

一种熔滴过渡特征信息提取和分析的新方法

利用同步采集系统采集了脉冲熔化极氩弧焊(P-MGAW)熔滴过渡过程的电信息和光谱信息。在反映熔滴过渡特征方面,光谱信息变化最为显著,信号强度变化占总强度的47.5％。电弧光谱信息中具有焊丝端头熔化、熔滴长大、缩颈和分离等过程的明显特征。采用Daubechies和Symlets离散小波分别分析了P-MGAW熔滴过渡过程的电信息和光谱信息后发现:电信息和光谱信息在高频细节分量中包含完全对应的熔滴分离特征信息,同时还含有熔滴过渡过程的其他细节特征。     

A neuro-fuzzy approach for increasing productivity in gas metal arc welding processes

This paper focusses on a study carried out in order to increase productivity in gas metal arc welding (GMAW) processes by optimising the deposition rate of the filler metal. To reach this aim, a possible solution was found in developing an adaptive system that is able to control and keep the wire feed speed constant at a desired and optimal value. This control has been accomplished by regulating an opportune variable typical of the welding process; in this case, the attention was focussed on the welding current intensity. Typical difficulties of GMAW processes, due above all to the great number of main variables and to their interdependence, suggested the possible solution by modelling a fuzzy-logic-based system, whose elements were determined by training an artificial neural network (ANN) with experimental data, obtained from bead on plate welds. At the same time, mathematical models, based on multiple regression analysis, were developed from the same data, in order to provide a comparison term and to assess the effectiveness of the neuro-fuzzy approach versus the mathematical methods. The results of this study confirmed the effectiveness of the proposed approach in the development of an integrated welding system in order to increase productivity.     

Adaptive volumetric heat source models for laser beam and laser + pulsed GMAW hybrid welding processes

Laser + pulsed gas metal arc welding (GMAW) hybrid welding process is an attractive joining technology in industry due to its synergy of the two processes. It is of great significance to conduct fundamental investigations involving mathematical modeling and understanding of the hybrid welding process. In this study, an adaptive heat source model is first developed for laser beam welding. Through combining the ray-tracing method with the keyhole profile determination technique based on the local energy balance, the keyhole shape and size are calculated and correlated to the distribution parameters of the volumetric heat source model. Then, thermal action characteristics in laser + pulsed GMAW hybrid welding are considered from viewpoint of macro-heat transfer, and a combined volumetric heat source model for hybrid welding is developed to take consideration of heat input from laser, pulsed gas metal arc, and overheated droplets. Numerical analysis of thermal conduction in hybrid welding is conducted. The shape and size of fusion zone and weld dimension in the quasi-steady state are calculated for various hybrid welding conditions, which have a fair agreement with the experimental results.     

Unsupervised droplet identification during the pulsed laser enhanced GMAW process

Pulsed laser-enhanced gas metal arc welding (GMAW) is an innovative arc welding process developed recently at the University of Kentucky. It uses the recoil pressure force generated by a pulsed laser to provide an additional force to reduce the needed electromagnetic detaching force (which is produced by the current) to assure the detachment of the droplet and the arc stability. The reduction in the current needed to detach droplets and maintain the arc stability improves the controllability of the most widely used arc welding process—GMAW—and its application range. To accurately control the detachment of the droplets, the force generated by the pulsed laser needs to be computed. Since this force is proportional to the distance from the center of the droplet to the welding wire, the problem can thus be changed to compute the distance between the droplet and the wire. To compute this distance, image processing method is the most effective way. Hence, different well-known image processing algorithms are implemented to address this problem and their performances are evaluated in this paper. Considering the robustness, processing speed, and automation, none of the evaluated image processing methods produce an acceptable result. To solve this specific problem, a novel image processing method is proposed. It is unsupervised and fast. Experimental results indicate that this proposed method can also achieve adequate computation accuracy.     

A new model of the antifriction effect on wiredrawing with ultrasound

In this paper, two different evolutionary algorithm-based neural network models were developed to optimise the unit production cost. The hybrid neural network models are, namely, genetic algorithm-based neural network (GA-NN) model and particle swarm optimization-based neural network (PSO-NN) model. These hybrid neural network models were used to find the optimal cutting conditions of Ti[C,N] mixed alumina-based ceramic cutting tool (CC650) and SiC whisker-reinforced alumina-based ceramic cutting tool (CC670) on machining glass fibre-reinforced plastic (GFRP) composite. The objective considered was the minimization of unit production cost subjected to various machine constraints. An orthogonal design and analysis of variance was employed to determine the effective cutting parameters on the tool life. Neural network helps obtain a fairly accurate prediction, even when enough and adequate information is not available. The GA-NN and PSO-NN models were compared for their performance. Optimal cutting conditions obtained with the PSO-NN model are the best possible compromise compared with the GA-NN model during machining GFRP composite using alumina cutting tool. This model also proved that neural networks are capable of reducing uncertainties related to the optimization and estimation of unit production cost.     

Application of particle swarm optimisation in artificial neural network for the prediction of tool life

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operations. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of a back-propagation algorithm and it is proven that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method .     

Application of particle swarm optimisation in artificial neural network for the prediction of tool life

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operation. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of the back-propagation algorithm and it is proved that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method.     

A study on mechanical characteristics of the friction stir welded A6005-T5 extrusion

In this paper, A6005-T5 extruded aluminum alloy sheets which are used for floor, roof or wall panels of railroad vehicles were welded by the friction stir welding (FSW) and gas metal arc welding (GMAW) techniques. The mechanical characteristics including the tensile strength, micro-hardness and fatigue strength of the FSW joint were compared to those of the base metal and GMAW joints. In order to determine the relationship between the welding variables of FSW and the mechanical characteristics of the joint, the response function was derived using the least square method and the sensitivity analysis was performed. The rotational speed, welding speed and tilting angle of the welding tool were chosen as design variables. On the basis of the Plackett-Burman design table, eight different FSW experiments were done, and then the effects of design variables on the mechanical characteristics of the FSW joint were analyzed. The result showed that the welding speed has a most significant effect on the tensile and fatigue strength. In the case of the micro-hardness, the effect of the tilting angle was the biggest.     

Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion

This paper used multi-sensor information fusion technology in pulsed gas tungsten arc welding. Arc sensor, visual sensor, and sound sensor were used simultaneously to obtain weld current, voltage, weld pool image, and weld sound information about the pulsed gas tungsten arc welding process, and special algorithms were designed to extract the respective signal features of different sensors’ information. Then D-S evidence theory was used to fuse the different signal features to predict the penetration status about the welding process. Aimed at the difficulty of obtaining basic probability assignment in D-S evidence theory, back-propagation (BP) neural network was used to obtain the basic probability assignment. Experiments were done to obtain data for training the BP neural network and test the prediction reliability of D-S evidence theory information fusion, and comparison results showed that D-S evidence theory could effectively use the information obtained by different sensors and obtain better prediction result than single sensor.     

电子束焊熔深神经网络模糊控制系统建模与仿真

研究钛合金电子束焊熔深控制系统建模问题。在分析电子束焊接特点的基础上,设计三因素五水平正交试验,通过试验得到不同焊接参数下熔宽和熔深的值,将熔宽和熔深的值作为训练样本对神经网络进行训练,建立以熔宽为输入,以熔深为输出的误差反向传播(Error back propagation,BP)神经网络模型,该模型由一个S型函数隐含层加上一个线性输出层组成。针对熔深数学模型难以获得的情况,设计以熔深的偏差和偏差变化率为输入变量,焊接电流的变化量为输出变量的模糊控制器,该控制器有9条模糊控制规则。将BP神经网络模型和模糊控制器结合起来建立钛合金电子束焊熔深控制系统模型,并且采用单位阶跃信号对该模型进行仿真试验,试验结果表明所设计的控制系统动态性能和稳态性能良好。     

Wire melting rate of alternating current gas metal arc welding

The wire melting phenomenon in alternating current gas metal arc welding (AC-GMAW) process should be carefully observed and analyzed since it is one of the most important representative characteristics of GMAW process. In this study, a new form of wire melting rate equation for AC-GMAW process is proposed based on energy conservation theory and arc physics. Using experimental data, the wire melting rate coefficients of AC-GMAW are obtained through nonlinear regression analysis. The wire melting rate is influenced not only by the current waveform, electrode polarity, and droplet size but also by the shape of the wire tip. That is, if the wire tip becomes more slender, arc heating has more influence on the wire melting. Using the wire melting rate proposed in this research, the uncertainty of calculating wire melting rate coefficients of AC-GMAW can be excluded comparing to existing method.     

Estimation and control of droplet size and frequency in projected spray mode of a gas metal arc welding (GMAW) process

New estimators are designed based on the modified force balance model to estimate the detaching droplet size, detached droplet size, and mean value of droplet detachment frequency in a gas metal arc welding process. The proper droplet size for the process to be in the projected spray transfer mode is determined based on the modified force balance model and the designed estimators. Finally, the droplet size and the melting rate are controlled using two proportional-integral (PI) controllers to achieve high weld quality by retaining the transfer mode and generating appropriate signals as inputs of the weld geometry control loop.