Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm

Flux cored arc welding (FCAW) process is a fusion welding process in which the welding electrode is a tubular wire that is continuously fed to the weld area. It is widely used in industries and shipyards for welding heavy plates. Welding input parameters play a very significant role in determining the quality of a weld joint. This paper addresses the simulation of weld bead geometry in FCAW process using artificial neural networks (ANN) and optimization of process parameters using particle swarm optimization (PSO) algorithm. The input process variables considered here include wire feed rate (F); voltage (V); welding speed (S) and torch Angle (A) each having 5 levels. The process output characteristics are weld bead width, reinforcement and depth of penetration. As per the statistical design of experiments by Taguchi L₂₅ orthogonal array, bead on plate weldments were made. The experimental results were fed to the ANN algorithm for establishing a relationship between the input and output parameters. The results were then embedded into the PSO algorithm which optimizes the process parameters subjected to the objectives. In this study the objectives considered are maximization of depth of penetration, minimization of bead width and minimization of reinforcement.     

A neural network and multiple regression method for the characterization of the depth of weld penetration in laser welding based on acoustic signatures

The need for the control of the depth of weld penetration has been and remains of a long term interest in the automated welding process. In this study, the relationship between the depth of weld penetration and the acoustic signal acquired during the laser welding process of high strength steels is investigated. The acoustic signals are first preprocessed by the spectral subtraction noise reduction method and analyzed both in the time domain and frequency domain. Based on this analysis, two algorithms are developed to acquire the acoustic signatures. The acquired acoustic signatures are then used to characterize the depth of weld penetration by using a neural network and a multiple regression analysis. The results show that the acoustic signatures can characterize and predict the depth of weld penetration well under different laser welding parameters.     

热膨胀电极位移法用于机器人点焊过程的研究

本文首次将热膨胀电极位移法用于机器人点焊．采用 高精度、高分辨率的光栅位移传感系统，对机器人点焊电极位移变化规律进行了研究．试验 结果表明，测量数据准确，重复性高，为电极位移法用于机器人点焊质量监控提供了新的途 径．     

Closed-Loop Control of Robotic Arc Welding System with Full-penetration Monitoring

The real-time detection of the state of the gap and weld penetration control are two fundamental issues in robotic arc welding. However, traditional robotic arc welding lacks external information feedback and the function of real-time adjusting. The objective of this research is to adopt new sensing techniques and artificial intelligence to ensure the stability of the welding process through controlling penetration depth and weld pool geometry. A novel arc welding robot system including function modules (visual modules, data acquisition modules) and corresponding software system was developed. Thus, the autonomy and intelligence of the arc welding robot system is realized. Aimed at solving welding penetration depth, a neural network (NN) model is developed to calculate the full penetration state, which is specified by the back-side bead width (Wb), from the top-side vision sensing technique. And then, a versatile algorithm developed to provide robust real-time processing of images for use with a vision-based computer control system is discussed. To this end, the peak current self adaptive regulating controller with weld gap compensation was designed in the robotic arc welding control system. Using this closed-loop control experiments have been conducted to verify the effectiveness of the proposed control system for the robotic arc welding process. The results show that the standard error of the Wb is 0.124 regardless of the variations in the state of the gap.     

RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW)

Welding processes are considered as an essential component in most of industrial manufacturing and for structural applications. Among the most widely used welding processes is the shielded metal arc welding (SMAW) due to its versatility and simplicity. In fact, the welding process is predominant procedure in the maintenance and repair industry, construction of steel structures and also industrial fabrication. The most important physical characteristics of the weldment are the bead geometry which includes bead height and width and the penetration. Different methods and approaches have been developed to achieve the acceptable values of bead geometry parameters. This study presents artificial intelligence techniques (AIT): For example, radial basis function neural network (RBF-NN) and multilayer perceptron neural network (MLP-NN) models were developed to predict the weld bead geometry. A number of 33 plates of mild steel specimens that have undergone SMAW process are analyzed for their weld bead geometry. The input parameters of the SMAW consist of welding current (A), arc length (mm), welding speed (mm/min), diameter of electrode (mm) and welding gap (mm). The outputs of the AIT models include property parameters, namely penetration, bead width and reinforcement. The results showed outstanding level of accuracy utilizing RBF-NN in simulating the weld geometry and very satisfactorily to predict all parameters in comparison with the MLP-NN model.

     

Prediction and optimization of depth of penetration for stainless steel gas tungsten arc welded plates using artificial neural networks and simulated annealing algorithm

The quality of a weld joint is highly influenced by depth of penetration. Hence, accurate prediction and maximization of depth of penetration is highly essential to ensure a good-quality joint. This paper highlights the development of neural network model for predicting depth of penetration and optimizing the process parameters for maximizing depth of penetration using simulated annealing algorithm. The process parameters chosen for the study are welding current, welding speed, gas flow rate and welding gun angle. The chosen output parameter was depth of penetration. The experiments were conducted based on design of experiments using fractional factorial with 125 runs. Using the experimental data, feed-forward backpropagation neural network model was developed and trained using Levenberg–Marquardt algorithm. It was found that ANN model based on network 4-15-1 predicted depth of penetration more accurately. A mathematical model was also developed correlating the process parameters with depth of penetration for doing optimization. A source code was developed in MATLAB to do the optimization. The optimized process parameters gave a value of 3.778 mm for depth of penetration.     

Robotic Welding Systems with Vision-Sensing and Self-learning Neuron Control of Arc Welding Dynamic Process

This paper addresses the vision sensing and neuron control techniques for real-time sensing and control of weld pool dynamics during robotic arc welding. Current teaching playback welding robots are not provided with this real-time function for sensing and control of the welding process. In our research, using composite filtering technology, a computer vision sensing system was established and clear weld pool images were captured during robotic-pulsed Gas Tungsten Arc Welding (GTAW). A corresponding image processing algorithm has been developed to pick up characteristic parameters of the weld pool in real-time. Furthermore, an ANN model of the weld pool dynamic process of robotic-pulsed GTAW was developed. Based on neuron self-learning PSD controller design, the real-time control of weld pool dynamics during the pulsed GTAW process has been realized in robotic systems.     

Control of dynamic keyhole welding process

Weld joint penetration control is a basic research topic in the welding research community. The authors propose using an innovative plasma arc welding process referred to as the quasi-keyhole process to achieve less application-dependent weld joint penetration sensing and control. To control the quasi-keyhole process, the peak current and keyhole sustaining current are selected as the control variables to maintain the keyhole establishment and sustaining periods at desired values. The dynamic quasi-keyhole process is approximated by a linear model with interval parameters. A control algorithm has been developed for the multivariable interval quasi-keyhole process based on a predictive control algorithm for interval SISO models. Experiments have been conducted to test the effectiveness of the control system developed.     

Application of sensing techniques and artificial intelligence-based methods to laser welding real-time monitoring: A critical review of recent literature

Laser welding has been widely utilized in various industries. Effective real-time monitoring technologies are critical for improving welding efficiency and guaranteeing the quality of joint-products. In this paper, the research findings and progress in recent ten years for real-time monitoring of laser welding are critically reviewed. Firstly, different sensing techniques applied for welding quality monitoring are reviewed and discussed in detail. Then, the advanced technologies based on artificial intelligence are summarized which are exploited to realize varied objectives of monitoring such as process parameter optimization, weld seam tracking, weld defects classification, and process feedback control. Finally, the potential research problems and challenges based on real-time intelligent monitoring are discussed, such as intelligent multi-sensor signal acquisition platform, data depth fusion method and adaptive control technology. This fundamental work aims to review the research progress in laser welding monitoring and provide a basis for follow-on research.     

球罐焊接微机控制系统

针对球罐这种对使用安全和质量有较高要求的特殊容器,提出了特别适合球罐焊接加工的微机控制系统。它根据操作者输入的有关参数,自动调整焊接电流、焊接速度等主要工艺参数,使焊机始终工作在最佳状态;还能对焊丝用量和焊缝长度进行独立和累计运算。这不仅保证了球罐的生产工艺和质量要求,而且提高了焊接效率。该控制系统对其他制造业的焊接也有一定的参考价值。     

Deep learning-empowered digital twin for visualized weld joint growth monitoring and penetration control

This paper presents an innovative digital twin to monitor and control complex manufacturing processes by integrating deep learning which offers strong feature extraction and analysis abilities. Taking welding manufacturing as a case study, a deep learning-empowered digital twin is developed as the visualized digital replica of the physical welding for joint growth monitoring and penetration control. In such a system, the information available directly from sensors including weld pool images, arc images, welding current and arc voltage is collected in pulsed gas tungsten arc welding (GTAW-P). Then, the undirect information charactering the weld joint geometry and determining the welding quality, including the weld joint top-side bead width (TSBW) and back-side bead width (BSBW), is computed/estimated by traditional image processing methods and deep convolutional neural networks (CNNs) respectively. Compared with single image source, weld pool image or arc image, the CNN model performs better when taking the 2-channel composite image combined by both as the input and the state-of-the-art accuracy in BSBW prediction with mean square error (MSE) as 0.047 mm² is obtained. Then, a decision-making strategy is developed to control the welding penetration to meet the quality requirement and applied successfully in various welding conditions. By modeling the weld joint cross section as an ellipse, the developed digital twin is visualized to offer a graphical user interface (GUI) for users perceiving the weld joint growth intuitively and effectively.     

A constant plunge depth control strategy for robotic FSW based on online trajectory generation

Robotic friction stir welding (RFSW) usually comes with a huge upsetting force, and the stiffness of the welding system distributes unevenly over the position, which leads to a large deviation of the plunge depth of the tool at the end of the robot. The conventional constant distance tracking control suffers from the problem of unsmooth compensation leading to the vibration of the robot and thus degrading the weld quality. For this problem, a constant plunge depth control based on online trajectory generation for RFSW is studied, which can generate an accurate welding trajectory according to the rough initial reference path and smoothly compensate for the plunge deviation. Initially, three laser-ranging sensors are utilized to measure the pose deviation of the tool in real-time and generate the ideal welding trajectory according to the projection vector method. Then, a deformation compensation model is established to realize the real-time prediction of the correct value. To ensure the smoothness and rapidity of the dynamic tracking process of displacement deviation, we adopt an online trajectory generator as the core of optimization control to meet the process constraints such as speed, acceleration, and jerk during the compensation process. Finally, simulation and experiment are carried out. The results show that the proposed method can effectively reduce the vibration caused by compensation during the welding process and reduce flash, which can improve the welding quality.     

Predicting the depth of penetration and weld bead width from the infra red thermal image of the weld pool using artificial neural network modeling

It is necessary to estimate the weld bead width and depth of penetration using suitable sensors during welding to monitor weld quality. Among the vision sensors, infra red sensing is the natural choice for monitoring welding processes as welding is inherently a thermal processing method. An attempt has been made to estimate the weld bead width and depth of penetration from the infra red thermal image of the weld pool using artificial neural network models during A-TIG welding of 3?mm thick type 316 LN stainless steel plates. Real time infra red images were captured using IR camera for the entire weld length during A-TIG welding at various current values. The image features such as length and width of the hot spot, peak temperature, and other features using line scan analysis are extracted using image processing techniques corresponding to particular locations of the weld joint. These parameters along with their respective current values are used as inputs while the measured weld bead width and depth of penetration are used as output of the neural network models. Accurate ANN models predicting weld bead width (9-11-1) and depth of penetration (9-9-1) have been developed. The correlation coefficient values obtained were 0.98862 and 0.99184 between the measured and predicted values of weld bead width and depth of penetration respectively.     

Multi-objective optimal design of small scale resistance spot welding process with principal component analysis and response surface methodology

This paper investigates the effects of welding parameters on the welding quality and optimizes them in the small scale resistance spot welding (SSRSW) process. Experiments are carried out on the basis of response surface methodology technique with different levels of welding parameters of spot welded titanium alloy sheets. Multiple quality characteristics, namely signal-to-noise (S/N) ratios of weld nugget diameter, penetration rate, tensile shear load and the failure energy, are converted into an independent quality index using principal component analysis. The mathematical model correlating process parameters and their interactions with the welding quality is established and discussed. And then this model is used to select the optimum process parameters to obtain the desired welding quality. The verification test results demonstrate that the method presented in this paper to optimize the welding parameters and enhance the welding performance is effective and feasible in the SSRSW process.     

Intelligent modelling of back-side weld bead geometry using weld pool surface characteristic parameters

In manual welding process, skilled welders can ensure the weld quality through compensating for deviation observed from the weld pool surface. In this paper a three dimensional vision sensing system was used to mimic the human vision system to observe the three-dimensional weld pool surface in pipe GTAW process. Novel characteristic parameters containing information about the penetration state specified by its back-side weld pool width and height were proposed based on the reconstructed three dimensional weld pool surfaces. Then, variation in characteristic parameters and their relationships with the back-side parameters were studied through experiments under different welding conditions. Direct measurement of penetration is not preferred in a manufacturing site, soft-sensing method was thus proposed as an alternative to obtain it in real time due to established soft-sensing model and auxiliary variables which can be sensed in real time. In order to obtain the penetration status in real time conveniently, back-propagation neural network, principle component analysis based back-propagation neural network and global best adaptive mutation particle swarm optimization based back-propagation neural network models were established to estimate the penetration based on the proposed characteristic parameters. It was found that the top-side characteristic parameters proposed can reflect the back-side weld pool parameters accurately and the models are capable of predicting the penetration status in real time by observing the three-dimensional weld pool surface.     

An auto-programming system of MAG welding parameters for vision-based robot

The on-line auto-programming of MAG welding parameters for a vision-based robot mainly aims at improving the adaptability of a welding robot in a complex welding environment, and it is a new kind of intelligent control method of weld quality. The authors developed an experimental system of an MAG welding robot with 3-D vision according to the requirements of real-time auto-programming of welding parameters. In the system, the scam geometry parameters, i.e., root gap and root face in a single V-groove, are used as input variables, while the welding process parameters, i.e., welding current and speed, are used as output variables. The relation between input and output variables is described by a fuzzy model. Experimental results show that the system can result in increased weld quality.     

Sensor-based control systems for arc welding robots

Arc welding is one of the most important areas of application for industrial robots. In most manufacturing situations, uncertainties in dimensions of the part, geometry of the joint, and the welding process itself make the use of sensors essential to maintaining weld quality. In this paper three types of control systems for arc welding robots are described: (1) tracking systems for centering the weld puddle over the joint, (2) weld process controls for maintaining proper seam width and penetration, and (3) supervisory controls for sequencing welding operations. These control functions have been implemented successfully in production using a computer vision sensor integrated into the welding torch. Experimental results are presented demonstrating the capabilities of the system.     

自动控制在薄板钛合金激光焊接中的应用

采用YAG激光作为焊接热源,针对具有自动控制的激光焊接系统,研究了1.3mm厚的TC4钛合金薄板的双面激光焊接.分析了试样的焊前清洗、保护气体的选择及激光工艺参数(激光功率密度、焊接速度、脉冲宽度、频率等)的匹配对焊缝形成及表观形貌的影响.试验结果表明,施焊前对试样进行合适的清洗十分必要,未经清洗的试样很难实现焊接成功.在保护气体的选择方面,使用氩气的焊接效果明显优于使用氮气;激光功率密度、焊接速度、脉冲宽度和脉冲频率4个参数的合理组合,是实现钛合金薄板焊接的关键因素.