A Study on Experimental Design Methods for the Automatic GMA Welding Process

2019 ◽  
Vol 294 ◽  
pp. 119-123
Author(s):  
Zong Liang Liang ◽  
Tae Jong Yun ◽  
Won Bin Oh ◽  
Bo Ram Lee ◽  
Ill Soo Kim
Keyword(s):  
Experimental Design ◽  
Taguchi Method ◽  
Arc Welding ◽  
Gma Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Welding Current ◽  
Welding Parameters ◽  
Bead Width ◽  
Arc Welding Process

Generally, the welding parameters directly affect the weld forming and the joint performance. Because many parameters are involved in the automatic arc welding process, it is not realistic to use traditional experimental methods, such as full factorial design. Therefore, it is important to find out the good experimental design method to determine the welding parameters for optimal joint quality with a minimal number of experiments. Therefore, this study is aimed at investigating the effect of DOE (Design of Experiment) methods on bead width of mild steel parts welded by the automatic GMA (Gas Metal Arc) welding process. In this work, Taguchi method was used for studying the effect of the welding parameters on optimization of bead width, while Box-Behnken method was utilized to develop a mathematical model relating the bead width to welding parameters such as welding voltage, arc current, welding speed and CTWD (Contact Tip to Work Distance). The S/N (Signal-to-Noise) ratio and the ANOVA (Analysis of Variance) were employed to find the optimal bead width. Confirmation tests were carried out to validate the effectiveness of the Taguchi method. The experimental results show that welding current mainly affected the bead width. The predicted bead width of 3.12mm was in good agreement with the confirmation tests. With the regression coefficient analysis in the Box-Behnken design, a relationship between bead width and four significant welding parameters was obtained. A second-order model has also been established between the welding parameters and the bead width as welding quality. The developed model is adequate to navigate the design space.

Control of the weld quality using welding parameters in a robotic welding process

2018 ◽  
Vol 1 (87) ◽  
pp. 32-40 ◽  
Author(s):  
M.-H. Park ◽  
B.-J. Jin ◽  
T.-J. Yun ◽  
J.-S. Son ◽  
C.-G. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Arc Welding ◽  
Gma Welding ◽  
Welding Process ◽  
Welding Parameters ◽  
Automatic Welding ◽  
Welding Quality ◽  
Arc Welding Process ◽  
Intelligent Models ◽  
Welding System

Purpose: Since the welding automations have widely been required for industries and engineering, the development of the predicted model has become more important for the increased demands for the automatic welding systems where a poor welding quality becomes apparent if the welding parameters are not controlled. The automated welding system must be modelling and controlling the changes in weld characteristics and produced the output that is in some way related to the change being detected as welding quality. To be acceptable a weld quality must be positioned accurately with respect to the joints, have good appearance with sufficient penetration and reduce low porosity and inclusion content. Design/methodology/approach: To achieve the objectives, two intelligent models involving the use of a neural network algorithm in arc welding process with the help of a numerical analysis program MATLAB have been developed. Findings: The results represented that welding quality was fully capable of quantifying and qualifying the welding faults. Research limitations/implications: Welding parameters in the arc welding process should be well established and categorized for development of the automatic welding system. Furthermore, typical characteristics of welding quality are the bead geometry, composition, microstructure and appearance. However, an intelligent algorithm that predicts the optimal bead geometry and accomplishes the desired mechanical properties of the weldment in the robotic GMA (Gas Metal Arc) welding should be required. The developed algorithm should expand a wide range of material thicknesses and be applicable in all welding position for arc welding process. Furthermore, the model must be available in the form of mathematical equations for the automatic welding system. Practical implications: The neural network models which called BP (Back Propagation) and LM (Levenberg-Marquardt) neural networks to predict optimal welding parameters on the required bead reinforcement area in lab joint in the robotic GMA welding process have been developed. Experimental results have been employed to find the optimal algorithm to predict bead reinforcement area by BP and LM neural networks in lab joint in the robotic GMA welding. The developed intelligent models can be estimated the optimal welding parameters on the desired bead reinforcement area and weld criteria, establish guidelines and criteria for the most effective joint design for the robotic arc welding process. Originality/value: In this study, intelligent models, which employed the neural network algorithms, one of AI (Artificial Intelligence) technologies have been developed to study the effects of welding parameters on bead reinforcement area and to predict the optimal bead reinforcement area for lab joint in the robotic GMA welding process. BP (Back Propagation) and LM (Levenberg-Marquardt) neural network algorithm have been used to develop the intelligent model.

Effect of Gas Metal Arc Welding Parameters on Mechanical Properties for Carbon Steel ASTM A285 Grade A

2011 ◽  
Vol 341-342 ◽  
pp. 16-20
Author(s):  
Mongkol Chaisri ◽  
Prachya Peasura
Keyword(s):  
Carbon Dioxide ◽  
Tensile Strength ◽  
Carbon Steel ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Welding Parameters ◽  
Shielding Gas ◽  
Metal Arc Welding ◽  
Arc Welding Process

The research was study the effect of gas metal arc welding process parameters on mechanical property. The specimen was carbon steel ASTM A285 grade A which thickness of 6 mm. The experiments with full factorial design. The factors used in this study are shielding gas and voltage. The welded specimens were tested by tensile strength testing and hardness testing according to ASME boiler and pressure vessel code section IX 2007. The result showed that both of shielding gas and voltage had interaction on tensile strength and hardness at 95% confidential (P value < 0.05). Factors affecting the tensile strength are the most carbon dioxide and 27 voltage were tensile strength 213.43 MPa. And hardness maximum of 170.60 HV can be used carbon dioxide and 24 voltage. This research can be used as data in the following appropriate parameters to gas metal arc welding process.

Evaluation of the sound signal based on the welding current in the gas—metal arc welding process

2003 ◽  
Vol 217 (5) ◽  
pp. 483-494 ◽  
Author(s):  
M Čudina ◽  
J Prezelj
Keyword(s):  
Arc Welding ◽  
Short Circuit ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Welding Current ◽  
Sound Signal ◽  
Frequency Noise ◽  
Metal Arc Welding ◽  
Arc Welding Process ◽  
Good Agreement

In this paper sound generated during the gas-metal arc welding process in the short-circuit mode was studied. Theoretical and experimental analyses of the acoustic signals have shown that there are two main noise-generating mechanisms. The first mechanism generating characteristic sound impulses is arc extinction and arc ignition; the second noise-generating mechanism is the arc itself, which acts as an ionization sound source and produces mainly high-frequency noise of a low level. The sound signal is used for assessing and monitoring the welding process and for prediction of welding process stability and quality. A new algorithm based on the measured welding current was established for the calculation of emitted sound during the welding process. The algorithm was verified for different supply voltages and for different welding materials. The comparisons have shown that the calculated values are in good agreement with measured values of the sound signal.

Dynamic Modeling and Adaptive Control of the Gas Metal Arc Welding Process

1994 ◽  
Vol 116 (3) ◽  
pp. 405-413 ◽  
Author(s):  
Jae-Bok Song ◽  
David E. Hardt
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Weld Bead ◽  
Adaptive Control System ◽  
Bead Width ◽  
Metal Arc Welding ◽  
Arc Welding Process

Control of the welding process is a very important step in welding automation. Since the welding process is complex and highly nonlinear, it is very difficult to accurately model the process for real-time control. In this research, a discrete-time transfer function matrix model for gas metal arc welding process is proposed. This empirical model takes the common dynamics for each output and inherent process and measurement delays into account. Although this linearized model is valid only around the operating point of interest, the adaptation mechanism employed in the control system render this model useful over a wide operating range. Since welding is inherently a nonlinear and multi-input, multi-output process, a multivariable adaptive control system is used for high performance. The process outputs considered are weld bead width and depth, and the process inputs are chosen as the travel speed of the torch and the heat input. A one-step-ahead (or deadbeat) adaptive control algorithm combined with a recursive least-squares methods for on-line parameter estimation is implemented in order to achieve the desired weld bead geometries. Control weighting factors are used to maintain the stability and reduce excessive control effort. Some guidelines for the control design are also suggested. Command following and disturbance rejection properties of the adaptive control system for both SISO and MIMO cases are investigated by simulation and experiment. Although a truly independent control of the outputs is difficult to implement because of a strong output coupling inherent in the process, a control system for simultaneous control of bead width and depth was successfully implemented.

A Study on Welding Quality of Robotic Arc Welding Process Using Mahalanobis Distance Method

2013 ◽  
Vol 773-774 ◽  
pp. 759-765 ◽  
Author(s):  
Reenal Ritesh Chand ◽  
Ill Soo Kim ◽  
Ji Hye Lee ◽  
Jong Pyo Lee ◽  
Ji Yeon Shim ◽  
...  
Keyword(s):  
Process Parameters ◽  
Mahalanobis Distance ◽  
Arc Welding ◽  
Gma Welding ◽  
Welding Process ◽  
Experimental Results ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Welding Quality ◽  
Arc Welding Process

In robotic GMA (Gas Metal Arc) welding process, heat and mass inputs are coupled and transferred by the weld arc and molten base material to the weld pool. The amount and distribution of the input energy are basically controlled by the obvious and careful choices of welding process parameters in order to accomplish the optimal bead geometry and the desired mechanical properties of the quality weldment. To make effective use of automated and robotic GMA welding, it is imperative to predict online faults for bead geometry and welding quality with respect to welding parameters, applicable to all welding positions and covering a wide range of material thickness. To successfully accomplish this objective, two sets of experiment were performed with different welding parameters; the welded samples from SM 490A steel flats adopting the bead-on-plate technique were employed in the experiment. The experimental results of current and voltage waveforms were used to predict the magnitude of bead geometry and welding quality, and to establish the relationships between weld process parameters and online welding faults. MD (Mahalanobis Distance) technique is employed for investigating and modeling of GMA welding process and significance test techniques were applied for the interpretation of the experimental data. Statistical models developed from experimental results which can be used to control the welding process parameters in order to achieve the desired bead geometry based on weld quality criteria.

scholarly journals Parametric Optimization of the GMAW Welding Process in Thin Thickness of Austenitic Stainless Steel by Taguchi Method

2021 ◽  
Vol 11 (18) ◽  
pp. 8742
Author(s):  
Glauco Nobrega ◽  
Maria Sabrina Souza ◽  
Manuel Rodríguez-Martín ◽  
Pablo Rodríguez-Gonzálvez ◽  
João Ribeiro
Keyword(s):  
Stainless Steel ◽  
Taguchi Method ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Welding Parameters ◽  
Root Penetration ◽  
Automatic Welding ◽  
Bead Width ◽  
Welding Processes

In the present work, an analysis of different welding parameters was carried out on the welding of stainless-steel thin thickness tubes by the Gas Metal Arc Welding (GMAW) process. The influence of three main parameters, welding voltage, movement angle, and welding current in the quality of the welds, was studied through a specifically designed experimental process based on the establishment of three different levels of values for each of these parameters. Weld quality is evaluated using destructive testing (macrographic analysis). Specifically, the width and root penetration of the weld bead were measured; however, some samples have been disregarded due to welding defects outside the permissible range or caused by excessive melting of the base metals. Data are interpreted, discussed, and analyzed using the Taguchi method and ANOVA analysis. From the analysis of variance, it was possible to identify the most influential parameter, the welding voltage, with a contribution of 43.55% for the welding penetration and 75.26% for the bead width, which should be considered in the designs of automatic welding processes to improve the quality of final welds.

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