The present invention generally relates to a useful online monitoring system and method of user thereof for monitoring a welding process to determine weld quality, weld process stability, and weld geometry for each weld formed during a welding process. More particularly, the online monitoring system has a computer having a graphical user interface (GUI); at least one welding machine; a communication interface for interfacing communication between the at least one welding machine and the computer; a data acquisition system for acquiring welding signal data, the data acquisition system having an associated memory means; a quality management database for managing and storing acquired welding signal data; and a statistical signal processing system in communication with the data acquisition system for processing welding signal data stored in the data acquisition system associated memory means, and in further communication with the communication interface for communicating processed welding signal data to the GUI.

Online monitoring system and method for a short-circuiting gas metal arc welding process

A real-time monitoring system is developed for detecting abnormal conditions in robotic gas metal arc welding. The butt-joint test pieces with simulated large gaps are used to intentionally introduce step disturbance of welding conditions. During the welding process, the welding voltage and current signals are sampled and processed on-line to extract the characteristic information reflecting the process quality. After the first statistical processing, it is found that seven statistical parameters (the mean, standard deviation, coefficient of variance and kurtosis of welding voltage; the mean, coefficient of variance and kurtosis of welding current) show variations during the step disturbance. Through the second statistical processing of the means of the welding voltage for subgroups of continuous measurement, the statistical control chart is obtained, and an SPC (statistical process control)-based on-line identifying method is developed. Ten robotic welding experiments are conducted to verify the real-time monitoring system. It is found that the correct identification rates for normal and abnormal welding conditions are 100% and 95%, respectively.

https://iopscience.iop.org/article/10.1088/0957-0233/18/1/037/pdf

Real-time sensing and monitoring in robotic gas metal arc welding

Welding is one of the major metal-joining process employed in fabrication industries, especially in manufacturing of boilers and pressure vessels Control of weld quality is very important for such industries considering the severe operating conditions Industries are looking for some kind of real-time process monitoring/control that will ensure the weld quality online and prevent the occurrence of defects In this paper an attempt is made to establish a correlation between the current and voltage signatures with the good weld and weld with porosity and burn through defect during the welding of carbon steel using gas metal arc welding (GMAW) process Experimental setup has been established and experiments were conducted using a welding robot integrated with GMAW power source The experimental setup includes online current and voltage sensors, data loggers, and signal processing hardware and software Welding conditions are carefully designed to produce good weld and weld with defects such as burn through and porosity Current and voltage signatures are captured using data acquisition system (DAS) Software has been developed to analyze the data captured by the DAS Statistical methods are employed to study the transient data The probability density distributions of the current and voltage signature demonstrates a good correspondence between the current and voltage signatures with the welding defect

Establishing Correlation Between Current and Voltage Signatures of the Arc and Weld Defects in GMAW Process

Evaluation of welding skill using probability density distributions and neural network analysis

Process parameters modeling have always been one of the key aspects in development of an adaptive control of arc welding process. The welding process parameters are inherently nonlinear, time-delayed, and interdependent, and their on-time adjustment highly influences a sound weld bead formation and process monitoring. During the welding process, parameters control is the primary goal to leads a quality welding. Moreover, the final weld joint behavior, i.e., residual stress, welding strength, and micro-crack formation are generally observed after cooling of the weld product. Thus, it has always been a difficult task to control mechanical properties of a final weld joint. To obtain the best mechanical properties, the final weld joint characteristics needed to be controlled and predicted during the process itself by precise adjustment of the process parameters. The paper presents a neuro-fuzzy modeling approach to provide adaptive control for the automatic process parameter adjustment. Three input parameters wire feed speed, welding gap, and torch speed are modeled with welding current output, providing control over weld bead formation during the welding. The same input process parameters are also modeled to predict final weld joint characteristics, i.e., dilution ratio, hardness of weld bead, hardness of fused zone, and bead width. In order to ascertain the effectiveness of the neuro-fuzzy modeling approach, multiple regression models were also developed to compare the performances.

Modeling of multiple characteristics of an arc weld joint.

This paper introduces a fuzzy logic system that is able to recognize common disturbances during automatic gas metal arc welding (GMAW) using measured welding voltage and current signals. A statistical method was employed to process the captured transient raw data, and the probability density distributions (PDDs) and the class frequency distributions (CFDs) were obtained. Based on the processed data (PDD values of welding voltage and current and CFD values of the short-circuiting timel, the system automatically generates fuzzy rules and membership functions of linguistic variables, conducts inference and defuzzification, and completes the evaluation process without further expert knowledge.

A fuzzy logic system for process monitoring and quality evaluation in GMAW: Monitoring weld quality in real time is proposed using voltage and current for raw data to develop graphic histograms

T. Polte

Papers

A fuzzy logic system for process monitoring and quality evaluation in GMAW: Monitoring weld quality in real time is proposed using voltage and current for raw data to develop graphic histograms