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.

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.

scholarly journals A Study on the Weldment Hardening Discrimination Procedure and Improvement of Flux Cored Arc Welding Process of ASTM A553-1 (9% Nickel Steel) Material Using Bead Geometry Distribution

Metals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1282
Author(s):  
Minho Park ◽  
Jisun Kim ◽  
Changmin Pyo ◽  
Jaewoong Kim ◽  
Kwangsan Chun
Keyword(s):  
Base Metal ◽  
Arc Welding ◽  
Welding Process ◽  
Fuel Oil ◽  
Bead Geometry ◽  
Nickel Steel ◽  
Welding Quality ◽  
Dilution Ratio ◽  
Flux Cored Arc Welding ◽  
Arc Welding Process

As a result of strengthened sulfur content standards for ship fuel oil in IMO regulations, major domestic and foreign carriers have a high and growing demand for liquefied natural gas (LNG) powered ships and related equipment. For LNG operation in a cryogenic environment, a storage tank and fuel supply system that uses steel with excellent brittleness and fatigue strength is required. Ships that use LNG have a high vulnerability to explosion and fire. For this reason, 9% Ni is typically used, since a ship requires high quality products with special materials and structural technologies that guarantee operability at cryogenic temperatures. However, there is an urgent need for research to derive a uniform welding quality, since high process difficulty and differences in welding quality related to a welder’s skills can cause a deterioration of the weld quality in the 9% Ni steel welding process. For 9% Ni steel, the higher the dilution ratio of the base metal, the lower the strength. In order to secure the required strength, excessive dilution of the base metal should be avoided, and the relationship between dilution ratio and strength should be investigated. According to previous research, if it exceeds 25% it may be lower than the API standard of 363 MPa for hardening welds. Therefore, in this study, the flux cored arc welding process is performed by establishing criteria that can be evaluated based on the SVM method in order to determine the structure of the weld to be cured according to the dilution rate of the base metal. We would like to propose a multipurpose optimization algorithm to ensure uniform quality of 9% Ni steel.

A Study on Development of the Optimal Neural Network in GMA Welding Process

2014 ◽  
Vol 936 ◽  
pp. 1759-1763
Author(s):  
Qian Qian Wu ◽  
Ji Hye Lee ◽  
Jong Pyo Lee ◽  
Min Ho Park ◽  
Young Su Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Process Parameters ◽  
Gma Welding ◽  
High Reliability ◽  
Welding Process ◽  
Welding Current ◽  
Welding Parameters ◽  
Welding Quality ◽  
Optimal Parameters ◽  
On Line

Gas Metal Arc (GMA) welding is considered as a multi-parameter process that it’s hard to find optimal parameters for good welding. To overcome the problem, an artificial neural network based on the backpropagation algorithm was built to realize the relationships between process parameters and welding quality as output parameter. In this study, Mahalanobis Distance (MD) was employed to evaluate the availability of a given welding parameters which was proved to performance well in multivariate statistics. Input parameters such as welding current and arc voltage were chosen due to their significant influence on the welding quality. To improve the precision of given parameters’ evaluation, neural networks with different configurations were verified. The analyses on the measured and predicted MD by the proposed neural network were conducted. The proposed neural network based on the error backpropogation algorithm was proved to have high reliability to evaluate process parameters, which further makes it available in on-line monitoring system.

The Optimal Levenberg-Marquardt Algorithm to Predict Welding Quality Using Mahalanobis Distance Theory

2014 ◽  
Vol 936 ◽  
pp. 1873-1877
Author(s):  
Ill Soo Kim ◽  
Qian Qian Wu ◽  
Ji Hye Lee ◽  
Jong Pyo Lee ◽  
Min Ho Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mahalanobis Distance ◽  
Gma Welding ◽  
Welding Process ◽  
Welding Parameters ◽  
Welding Quality ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
On Line

With the development of computational technology, neural network has attracted the more and more attentions to reveal the relationships between the process parameters and welding geometry. However, the Gas Metal Arc (GMA) welding is complex and of multiple interactions so that mathematical model for welding parameters has not been achieved. Neural networks have been noted as being particularly advantageous for modeling systems which contain noisy, fuzzy and uncertain elements, while a sufficient algorithm is employed. In this study, Levenberg-Marquardt algorithm was employed into GMA welding process. Mahalanobis Distance (MD) was measured to determine the on-line welding quality to avoid joint failure as welding quality. To get an optimal neural network, cases with different configurations were carried out. The Root of the Mean sum of Squared (RMS) error was adopted to evaluate the accuracy of the prediction by neural networks with LM algorithm. The results presented that the proposed algorithm had the superiority of high accuracy that can be used in the on-line welding process.

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.

A study on welding quality for the automatic vertical-position welding process based on Mahalanobis Distance method

2018 ◽  
Vol 1 (91) ◽  
pp. 31-40
Author(s):  
B.-J. Jin ◽  
M.-H. Park ◽  
T.-J. Yun ◽  
J.-Y. Shim ◽  
B.-Y. Kang ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Welding Process ◽  
Welding Current ◽  
Welding Parameters ◽  
Vertical Position ◽  
Welding Quality ◽  
Distance Method ◽  
Arc Voltage ◽  
On Line ◽  
Arc Welding Process

Purpose: The welding quality and reducing production cost could be achieved by developing the automatic on-line welding quality monitoring system. However, investigation of welding fault to quantify the welding quality on the horizontal-position welding has been concentrated. Therefore, MD (Mahalanobis Distance) method on the vertical-position welding process by analysing the transform arc voltage and welding current gained from the on-line monitoring system has been applied. Design/methodology/approach: The transformed welding current and arc voltage data were taken from the experiment whereby the data number was 2500 data/s. The prediction of Contact Tip to Work Distance (CTWD) to gain best welding quality using the waveform variations were then taken from the experimental results. MD was employed to quantify the welding quality by analysing the transformed arc voltage and welding current. Finally, the optimal CTWD setting has verified the developed algorithms through additional experiments. Two kinds of experiments has been carried out by changing welding parameters artificially to verify the sensitivity and feasibility of WQ (Welding Quality) based on the concepts of MD and normal distribution. Findings: The results represented that WQ was fully capable of quantifying and qualifying the welding faults for automatic vertical-position welding process. Research limitations/implications: The arc welding process on the vertical-position compared to a horizontal-position welding is much more difficult because the metal transfer is influenced by the gravity force. To solve the problem, a new algorithm to monitor and control the welding fault during the arc welding process has been developed. Furthermore, optimization of welding parameters for the vertical-position welding process was really difficult to use the developed algorithms because they are only useful in selecting stored data and not for evaluating the effect of the variation of welding parameters on the weld ability. Practical implications: The developed algorithm could be achieved the highest welding quality at 15mm CTWD setting which the welding quality is 99.50% for the start section and 99.68% at the middle section. Originality/value: This paper proposed a new algorithm which employed the concepts of MD (Mahalanobis Distance) and normal distribution to describe a good quality welding.

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