Real-time monitoring of weld penetration quality in robotic arc welding process

It is of great significance to develop an intelligent monitoring system for weld penetration defects such as incomplete penetration and burn-through in real-time during robotic arc welding process. In this paper, robotic gas metal arc welding experiments are carried out on the mild steel test pieces with Vee-type groove. Through-the-arc sensing method is used to capture the transient values of the welding voltage and current. The raw data of the captured welding current and voltage are processed statistically, and the feature vector SIO is extracted to correlate the welding conditions to the weld penetration information. It lays foundation for intelligent monitoring of weld quality in robotic arc welding.

Investigate Weld Quality of Gas Metal Arc Welding Process Through Online Monitoring of Input Parameters

Gas metal arc welding(GMAW)is also referred as the metal inert gas(MIG)welding which is a process of welding done by the formation of an electric arc between the consumable wire electrode and the workpiece.Through the welding process,a continuous flow of inert gas is supplied,and it avoids the weld being subjected to react with atmospheric air.The process can be automatic or semi-automatic where the main input parameters like current and the voltage can be direct and constant,respectively.Not only the current and voltage the welding quality depends on some more input parameters such as arc gap,velocity,and temperature.In this paper,we explain about a setup which is capable of real-time monitoring of input parameters mentioned above and selecting the best MIG welding parameters for the mild steel.The setup is composed of several sensors and microcontrollers for the collection and the measurement of the input parameters.The samples were categorized according to the federate and the voltage adjustment of the selected welding machine.Then the final objective was to identify the samples of the weld with different parameter changes which are monitored through the system.For the analysis,the samples were subjected to tensile and hardness tests,and microstructure tests to find the dependence of the input parameters which effect for the weld quality.Finally,the experimental results verified the effectiveness of the system for the selection of the quality weld.

The Improvement of Earthquake Real-Time Monitoring System of Chinese National Digital Seismic Network

The earthquake real-time monitoring system of the Chinese National Digital Seismic Network has been in operation since"the Ninth Five-year Plan"period,and the stability of the system has been well tested.In recent years,with the continuous improvement of monitoring technology and increase of public demands,the original real-time monitoring system needs to be upgraded and improved in terms of timeliness,stability,accuracy and ease of operation.Therefore,by accessing a total of more than 1,000 seismic stations,reducing the seismic trigger threshold of the monitoring system,eliminating the false trigger stations and optimizing the seismic waveform display interface,the current earthquake monitoring demands can be satisfied on the basis of ensuring the stable operation of the system.

A real-time AI-assisted seismic monitoring system based on new nodal stations with 4G telemetry and its application in the Yangbi M_(S) 6.4 aftershock monitoring in southwest China

A rapidly deployable dense seismic monitoring system which is capable of transmitting acquired data in real time and analyzing data automatically is crucial in seismic hazard mitigation after a major earthquake.However,it is rather difficult for current seismic nodal stations to transmit data in real time for an extended period of time,and it usually takes a great amount of time to process the acquired data manually.To monitor earthquakes in real time flexibly,we develop a mobile integrated seismic monitoring system consisting of newly developed nodal units with 4G telemetry and a real-time AI-assisted automatic data processing workflow.The integrated system is convenient for deployment and has been successfully applied in monitoring the aftershocks of the Yangbi M_(S) 6.4 earthquake occurred on May 21,2021 in Yangbi County,Dali,Yunnan in southwest China.The acquired seismic data are transmitted almost in real time through the 4G cellular network,and then processed automat-ically for event detection,positioning,magnitude calculation and source mechanism inversion.From tens of seconds to a couple of minutes at most,the final seismic attributes can be presented remotely to the end users through the integrated system.From May 27 to June 17,the real-time system has detected and located 7905 aftershocks in the Yangbi area before the internal batteries exhausted,far more than the catalog provided by China Earthquake Networks Center using the regional permanent stations.The initial application of this inte-grated real-time monitoring system is promising,and we anticipate the advent of a new era for Real-time Intelligent Array Seismology(RIAS),for better monitoring and understanding the subsurface dynamic pro-cesses caused by Earth's internal forces as well as anthropogenic activities.

Label-Free and Real-Time Monitor of Binding and Dissociation Processes between Protein A and Swine IgG by Oblique-Incidence Reflectivity Difference Method

Life science has a need for detection methods that are label-free and real-time. In this paper, we have selected staphylococcal protein A （SPA） and swine immunoglobulin G （IgG）, and monitor the bindings between SPA and swine IgG with different concentrations, as well as the dissociations of SPA-swine IgG complex in different pH values of phosphate buffer by oblique-incidence reflectivity difference （OIRD） in a label-free and real-time fashion. We obtain the ON and OFF reaction dynamic curves corresponding to the bindings and dissociations of SPA and swine IgG. Through our analysis of the experimental results, we have been able to obtain the damping coefficients and the dissociation time of SPA and swine IgG for different pH values of the phosphate buffer. The results prove that the OIRD technique is a competing method for monitoring the dynamic processes of biomolecule interaction and achieving the quantitative information of reaction kinetics.

Patient Centered Real-Time Mobile Health Monitoring System

In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.

Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions.This ideology is strengthened by Industry 4.0,which aims to continuously monitor high-value manufacturing assets.This article builds upon the Industry 4.0 concept to improve the efficiency of manufacturing systems.The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding(FSW)process.It consists of a CNC manufacturing machine,sensors,edge,cloud systems,and deep neural networks,all working cohesively in real time.The edge device,located near the FSW machine,consists of a neural network that receives sensory information and predicts weld quality in real time.It addresses time-critical manufacturing decisions.Cloud receives the sensory data if weld quality is poor,and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine.Several experiments are conducted for training the neural networks.The framework successfully tracks process quality and improves the welding by controlling it in real time.The system enables faster monitoring and control achieved in less than 1 s.The framework is validated through several experiments.

Process disturbances monitoring and recognition of short-circuiting GMAW by fuzzy c-means system

An intelligent fuzzy c-means system for process monitoring and recognition of process disturbances during short- circuiting gas metal arc welding （GMAW） is introduced in this paper. The raw measured and statistically test data of probability density distribution （ PDD ） and class frequency distribution （ CFD ） of welding electrical parameters are further processed into a 7-dimensional array which is designed to describe various welding conditions, and is employed as input vector of the intelligent fuzzy c-means system. The fuzzy c-means system is used to conduct process monitoring and automatic recognition. The correct recognition rate of 24 test data under 8 kinds of welding condition is 92%.

Measurement of Collision Conditions in Magnetic Pulse Welding Processes

MPW （magnetic pulse welding） is a solid state joining technology that allows for the generation of strong metallic bonds, even between dissimilar metals. Due to the absence of external heat, critical intermetallic phases can largely be avoided. In this process, Lorentz forces are utilized for the rapid acceleration of at least one of the two metallic joining partners leading to the controlled high velocity impact between them. The measurement of the collision conditions and their targeted manipulation are the key factors of a successful process development. Optical measuring techniques are preferred, since they are not influenced by the prevalent strong magnetic field in the vicinity of the working coil. In this paper, the characteristic high velocity impact flash during MPW was monitored and evaluated using phototransistors in order to measure the time of the impact. The results are in good accordance with the established PDV （photon Doppler velocimetry） and show a good repeatability. Furthermore, the collision front velocity was investigated using adapted part geometries within a series of tests. This velocity component is one of the key parameters in MPW; its value decreases along the weld zone. With the help of this newly introduced measurement tool, the magnetic pressure distribution or the joining geometry can be adjusted more effectively.

A machine vision approach to seam tracking in real-time in PAW of large-diameter stainless steel tube

Manual monitoring and seam tracking through watching weld pool images in real-time, by naked eyes or by industrial TV, are experience-depended, subjective, labor intensive, and sometimes biased. So it is necessary to realize the automation of computer-aided seam tracking. A PAW (plasma arc welding) seam tracking system was developed, which senses the molten pool and the seam in one frame by a vision sensor, and then detects the seam deviation to adjust the work piece motion adaptively to the seam position sensed by vision sensor. A novel molten pool area image-processing algorithm based on machine vision was proposed. The algorithm processes each image at the speed of 20 frames/second in real-time to extract three feature variables to get the seam deviation. It is proved experimentally that the algorithm is very fast and effective. Issues related to the algorithm are also discussed.

A vision tracking system of robot CO_2 short transfer welding

This paper expounds the necessity of applying real-time control in vision sensing and tracking system of welding robot and analyses the difficulty of welding image processing. Through experiments, a practical robot CO2 arc adaptive feedback tracking system is established. According to the analysing of current and voltage signals between welding torch and base metal, the image freezing time for TMS-32020 processor is determined, and the defect of dark image and serious splashes in CO, welding image are avoided. Thus welding image becomes clear, and digitalization of video signal is stability. Then, with adaptive threshold control the welding image binaryzation, 3×3 mean level filtration and 3×3 weighting mean level filtration in welding seam are processed.Furthermore, the deviation between the centre of welding torch and the seam welded is found out, even though there are much spatter in the welding image.At last, the end effector of the robot is controlled and a welding torch is carried to track the seam welded during arc welding.

Real-time monitoring system for quality monitoring of jujube slice during drying process

The real-time monitoring and prediction system for quality attributes of jujube slices during the drying process was designed to solve the problem of destructive and inconvenient of the traditional quality detection method and realize quality online monitoring.Firstly,machine vision and automatic weighing were employed to monitor the color and moisture content changes of jujube slices in real-time.Secondly,correlation models between color parameter(a^(*)value)and nutritional quality attributes(vitamin C,reducing sugar)were established to predict vitamin C and reducing sugar content of jujube slices during the drying process.Finally,the upper computer monitoring software was integrated and designed based on LABVIEW virtual instrument,and the real-time monitoring system was tested and validated.Results showed that:the changing trends of color(L^(*),a^(*),and b^(*)values)monitored by the system were basically the same as the results detected by color difference meter,and the average errors of L^(*),a^(*),and b^(*)values were 0.93,0.52,and 0.73,respectively.The average relative error of moisture content between the system monitoring and manual static detection was 0.18%.The average error of vitamin C and reducing sugar content between the system prediction and manual detection were 50 mg/100 g in dry basis and 0.71g/100 g in dry basis,respectively.The current work can provide a useful reference for real-time monitoring of quality attributes of fruits and vegetables during the drying process.

某超高层塔冠结构深化设计与制作安装分析

以某工程塔楼塔冠结构为例,对其钢桁架吊点构件多、跨度大、梁柱相交节点处深化设计较为复杂等难点问题进行了研究分析,并制定了超高层塔冠结构的合理分段方案及安装方法,缩短了工期,保证了产品加工质量,具有一定的推广价值。

基于焊接电信号频域特征的焊接过程稳定性评估

通过调节焊接电源的多组脉冲波形参数进行一系列焊接试验,对比并分析了脉冲电流峰值系数、脉冲电流峰值时间系数、脉冲电流基值系数、脉冲电流上升系数和脉冲电流下降系数发生改变后焊接电流信号的变化情况.采用集合经验模态分解(ensemble empirical mode decomposition,EEMD)方法对焊接过程中实时采集到的焊接电流信号进行分解,并对焊接电流信号和一系列本征模态函数(intrinsic mode function,IMF)进行时频域分析.随后,根据分析结果和焊缝表面形貌对焊接过程稳定性进行评估.结果表明,使用EEMD方法能够从焊接电流信号中分解出与短路过渡过程密切相关的特征IMF,稳定的焊接过程与不稳定的焊接过程相比,其特征IMF频谱分布区别明显,特征IMF频率分布范围越窄,焊接过程越稳定性,焊接飞溅越小,焊缝表面成形越好.

基于OPC UA技术的T型梁焊接机器人在线监测系统

针对薄板分段车间的焊接装备复杂化程度高、焊接作业监控困难、焊接信息反馈不及时等问题,研究薄板分段车间T型梁焊接机器人的工艺流程和工艺参数,开发一种基于对象链接与嵌入过程控制的统一架构[OLE(Object Linking and Embedding)for Process Control Unified Architecture, OPC UA]技术的T型梁焊接机器人在线监测系统。该系统主要对T型梁焊接机器人的生产进度、设备运行状态和中间产品质量等进行在线监测,实现数据实时采集和生产过程监控与分析等功能,可消除设备之间的信息孤岛现象,实现车间生产透明化,提高生产效率,确保焊接质量。

焊接过程监测技术研究现状

在当前制造业持续向信息化、数字化转型升级的背景下,我国焊接生产的转型升级步伐相对滞后,相关生产企业大多缺乏对焊接过程的在线监测。针对这一现状,围绕声、光、电3种焊接电弧物理信号,对国内焊接过程监测技术的研究现状进行综述和分析,指出了每种监测技术的特点和适用性,并展望了焊接监测技术的发展方向。

脉冲熔化极气体保护焊熔池图像的检测与处理

利用CCD摄像机和复合滤光技术建立了一套熔池图像实时采集系统。利用光谱分析,提出了适合于脉冲熔化极气体保护焊(P-GMAW)熔池成像的滤光光谱窗口,并对中心波长分别为665nm和1064nm滤光系统的成像质量进行了分析。分析了P-GMAW单个脉冲内的不同时刻的熔池图像,研究表明脉冲基值结束期间为最佳的取像时刻。使用中心波长为665nm的滤光系统,从熔池正后方取像,获取了清晰的熔池图像。针对所获取的典型的P-GMAW熔池图像,详细研究了其成像机理。开发了一套熔池图像处理程序,利用该程序提取了完整的熔池轮廓。

基于特征参数的CO_2弧焊电源的实时监测与评价

对CO2 焊接短路过渡过程进行准确测量和综合分析 ,是控制和稳定焊接过程 ,保证焊接质量的前提 ,也是研究焊接过程和进行焊接试验必不可少的工具。本文研制了一种用于CO2弧焊电源工作过程的监测评价系统 ,该系统通过对弧焊规范参数和熔滴短路过渡过程特征参数的实时检测、分析以及综合评判 ,实现对CO2 弧焊电源工作过程的监测与评价。

短路GMAW焊在线质量监测的多变量统计过程控制方法

熔化极保护气体焊(GMAW)广泛地应用在自动焊尤其是机器人焊接过程中,机器人焊接过程中的快节拍要求对焊接质量进行自动监测及备份。现采用焊接过程中的电信号,通过对过程电信号的处理,实现对被加工部件质量的在线判别。通过分析单变量统计过程控制(SPC)对焊接过程的质量监测,发现了其存在的局限性,并在此基础上,采用多变量统计过程控制(MSPC),实现了GMAW过程的在线质量监控;最后在自开发的在线监测系统上,试验验证MSPC方法的可行性。

潜艇耐压壳体埋弧焊过程参数计算机监测分析系统的研究

基于Windows 2 0 0 0 ,设计了潜艇耐压壳体埋弧焊过程参数计算机检测、记录和分析的软硬件系统。试验结果表明 ,该系统能较好地完成焊接电流和电弧电压信号的实时监测和记录 ,能及时捕捉到焊接参数的异常变化 ,能满足焊接生产长时间、不间断地实时监测和记录焊接工艺参数的需求 ,并能对焊接工艺参数进行分析 ,从而可对工艺参数的合理性进行评定。