A Study on Fuzzy Logic Theory to Predict the Process Parameters in GMA Welding Process

2006 ◽  
Vol 505-507 ◽  
pp. 541-546
Author(s):  
Il Soo Kim ◽  
Joon Sik Son ◽  
H.H. Kim ◽  
I.J. Kim ◽  
B.Y. Kang
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Gma Welding ◽  
Rapid Development ◽  
Welding Process ◽  
Bead Geometry ◽  
Bead Width ◽  
Fuzzy Logic Theory ◽  
Welding System

Recently, there has been a rapid development in computer technology, which has in turn led to develop the automated welding system using Artificial Intelligence (AI). However, the automated welding system has not been achieved duo to difficulties of the control and sensor technologies. In this paper, the classification of the optimized bead geometry such as bead width, height, penetration and bead area in the Gas Metal Arc (GMA) welding with fuzzy logic is presented. The Fuzzy C-Means (FCM) algorithm, which is best known an unsupervised fuzzy clustering algorithm is employed here to analysis the specimen of the bead geometry. Then the quality of the GMA welding can be classified by this fuzzy clustering technique, and the optimal bead geometry can also be achieved.

An Evaluation Approach for Prediction of Process Parameters with Genetic Algorithm

2008 ◽  
Vol 580-582 ◽  
pp. 375-378
Author(s):  
D.T. Thao ◽  
Il Soo Kim
Keyword(s):  
Genetic Algorithm ◽  
Process Parameters ◽  
Graphical Representation ◽  
Gma Welding ◽  
Welding Process ◽  
Bead Geometry ◽  
Bead Width ◽  
Evaluation Approach ◽  
Wide Range ◽  
Intelligent Model

Gas Metal Arc (GMA) welding process has widely been employed due to the wide range of applications, cheap consumables and easy handling. A suitable mathematical model to achieve a high level of welding performance and quality should be required to study the characteristics for the effects of process parameters on the bead geometry in the GMA welding process. The objective of this paper is to present development of three empirical models (linear, curvilinear and intelligent model) based on full factorial design with two replications to estimate process parameters on top-bead width in robotic GMA welding process. Regression analysis was employed for optimization of the coefficients of linear and curvilinear models, but Genetic Algorithm (GA) was utilized to estimate the coefficients of intelligent model. ANOVA analysis using experimental data were carried out representation of main and interaction effects between process parameters on top-bead width. Resulting solutions and graphical representation showed that the developed intelligent model can be used for prediction on top-bead width in robotic GMA welding process

scholarly journals Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1494
Author(s):  
Ran Li ◽  
Manshu Dong ◽  
Hongming Gao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Welding Speed ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Training Dataset ◽  
Welding Parameters ◽  
Bead Geometry ◽  
Bead Width ◽  
Artificial Neural

Bead size and shape are important considerations for industry design and quality detection. It is hard to deduce an appropriate mathematical model for predicting the bead geometry in a continually changing welding process due to the complex interrelationship between different welding parameters and the actual bead. In this paper, an artificial neural network model for predicting the bead geometry with changing welding speed was developed. The experiment was performed by a welding robot in gas metal arc welding process. The welding speed was stochastically changed during the welding process. By transient response tests, it was indicated that the changing welding speed had a spatial influence on bead geometry, which ranged from 10 mm backward to 22 mm forward with certain welding parameters. For this study, the input parameters of model were the spatial welding speed sequence, and the output parameters were bead width and reinforcement. The bead geometry was recognized by polynomial fitting of the profile coordinates, as measured by a structured laser light sensor. The results showed that the model with the structure of 33-6-2 had achieved high accuracy in both the training dataset and test dataset, which were 99% and 96%, respectively.

scholarly journals Experimental and Numerical Analysis on TIG Arc Welding of Stainless Steel Using RSM Approach

Metals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1659
Author(s):  
Sasan Sattarpanah Karganroudi ◽  
Mahmoud Moradi ◽  
Milad Aghaee Attar ◽  
Seyed Alireza Rasouli ◽  
Majid Ghoreishi ◽  
...  
Keyword(s):  
Heat Flux ◽  
Stainless Steel ◽  
Welding Speed ◽  
Arc Welding ◽  
Welding Process ◽  
Weld Bead ◽  
Bead Geometry ◽  
Depth Of Penetration ◽  
Weld Bead Geometry ◽  
Bead Width

This study involves the validating of thermal analysis during TIG Arc welding of 1.4418 steel using finite element analyses (FEA) with experimental approaches. 3D heat transfer simulation of 1.4418 stainless steel TIG arc welding is implemented using ABAQUS software (6.14, ABAQUS Inc., Johnston, RI, USA), based on non-uniform Goldak’s Gaussian heat flux distribution, using additional DFLUX subroutine written in the FORTRAN (Formula Translation). The influences of the arc current and welding speed on the heat flux density, weld bead geometry, and temperature distribution at the transverse direction are analyzed by response surface methodology (RSM). Validating numerical simulation with experimental dimensions of weld bead geometry consists of width and depth of penetration with an average of 10% deviation has been performed. Results reveal that the suggested numerical model would be appropriate for the TIG arc welding process. According to the results, as the welding speed increases, the residence time of arc shortens correspondingly, bead width and depth of penetration decrease subsequently, whilst simultaneously, the current has the reverse effect. Finally, multi-objective optimization of the process is applied by Derringer’s desirability technique to achieve the proper weld. The optimum condition is obtained with 2.7 mm/s scanning speed and 120 A current to achieve full penetration weld with minimum fusion zone (FZ) and heat-affected zone (HAZ) width.

scholarly journals Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning

Metals ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 451
Author(s):  
Martin A. Kesse ◽  
Eric Buah ◽  
Heikki Handroos ◽  
Godwin K. Ayetor
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Neural Network ◽  
Predictive Accuracy ◽  
Welding Process ◽  
Tig Welding ◽  
Bead Geometry ◽  
Bead Width ◽  
Modeling Tools ◽  
Welding Processes

Recent developments in artificial intelligence (AI) modeling tools allows for envisaging that AI will remove elements of human mechanical effort from welding operations. This paper contributes to this development by proposing an AI tungsten inert gas (TIG) welding algorithm that can assist human welders to select desirable end factors to achieve good weld quality in the welding process. To demonstrate its feasibility, the proposed model has been tested with data from 27 experiments using current, arc length and welding speed as control parameters to predict weld bead width. A fuzzy deep neural network, which is a combination of fuzzy logic and deep neural network approaches, is applied in the algorithm. Simulations were carried out on an experimental test dataset with the AI TIG welding algorithm. The results showed 92.59% predictive accuracy (25 out of 27 correct answers) as compared to the results from the experiment. The performance of the algorithm at this nascent stage demonstrates the feasibility of the proposed method. This performance shows that in future work, if its predictive accuracy is improved with human input and more data, it could achieve the level of accuracy that could support the human welder in the field to enhance efficiency in the welding process. The findings are useful for industries that are in the welding trade and serve as an educational tool.

Effect of Activating Flux on the A356 and 6061 Aluminum Alloy GMA Welds

2016 ◽  
Vol 835 ◽  
pp. 161-166 ◽  
Author(s):  
Hsuan Liang Lin ◽  
Wun Kai Wang
Keyword(s):  
Aluminum Alloy ◽  
Gma Welding ◽  
Welding Process ◽  
Hot Cracking ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
6061 Aluminum Alloy ◽  
Activating Flux ◽  
Dissimilar Welds ◽  
Grey Relational

The objective of this study is to investigate the effects of activating fluxes on the weld bead geometry, hot cracking susceptibility and mechanical property of A356 and 6061 aluminum alloy dissimilar welds in the gas metal arc (GMA) welding process. In this activated GMA welding process, there were nine single-component fluxes used in the initial experiment to evaluate the penetration capability of butt-joint GMA welds. The grey relational analysis (GRA) was employed to obtain the better weld bead geometry of welds that were considered with multiple quality characteristics. Based on higher grey relational grade (GRG), four single-component fluxes were selected to create mixed-component flux in the next stage. The experimental results showed that the GMA welds coated with activating flux were provided with better geometry of dissimilar welds. The experimental procedure of activated GMA welding process not only produced a significant increase in tensile strength of welds, but also improved the hot cracking susceptibility of aluminum alloy welds.

A Study of Software Approach for Predicting Weld Bead Geometry in Shielded Metal Arc Welding (SMAW) Process

2014 ◽  
Vol 554 ◽  
pp. 386-390
Author(s):  
C.W. Mohd Noor ◽  
Manuhutu Ferry ◽  
W.B. Wan Nik
Keyword(s):  
Arc Welding ◽  
Welding Process ◽  
Weld Bead ◽  
Bead Geometry ◽  
Shielded Metal Arc Welding ◽  
Depth Of Penetration ◽  
Weld Bead Geometry ◽  
Bead Width ◽  
Real Process ◽  
Metal Arc Welding

The prediction of the optimal weld bead width is an important aspect in shielded metal arc welding (SMAW) process as it is related to the strength of the weld. This paper focuses on investigation of the development of the simple and accurate model for prediction of weld bead geometry. The experiment used welding current, arc length, welding speed, welding gap and electrode diameter as input parameters. While output parameters are bead width, depth of penetration and weld reinforcement. A number of 33 mild steel plate specimens had undergone the SMAW welding process. The experimental data was used to develop mathematical models using SPSS software. The actual and predicted values of the weld bead geometry are compared. The proposed models shows positive correlation to the real process.

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.

Study on Fuzzy Clustering Algorithm of Spatial Data Mining

2013 ◽  
Vol 416-417 ◽  
pp. 1244-1250
Author(s):  
Ting Ting Zhao
Keyword(s):  
Data Mining ◽  
Fuzzy Clustering ◽  
Spatial Data ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Rapid Development ◽  
Spatial Database ◽  
Spatial Data Mining ◽  
Data Set ◽  
Fuzzy Similarity

With rapid development of space information crawl technology, different types of spatial database and data size of spatial database increases continuously. How to extract valuable information from complicated spatial data has become an urgent issue. Spatial data mining provides a new thought for solving the problem. The paper introduces fuzzy clustering into spatial data clustering field, studies the method that fuzzy set theory is applied to spatial data mining, proposes spatial clustering algorithm based on fuzzy similar matrix, fuzzy similarity clustering algorithm. The algorithm not only can solve the disadvantage that fuzzy clustering cant process large data set, but also can give similarity measurement between objects.

Parametric Optimization of TIG Welding on 316L Austenitic Stainless Steel by Grey-Based Taguchi Method

2011 ◽  
Vol 383-390 ◽  
pp. 4667-4671 ◽  
Author(s):  
Nanda Naik Korra ◽  
K.R. Balasubramanian
Keyword(s):  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Gas Flow ◽  
Welding Process ◽  
Weld Bead ◽  
Bead Geometry ◽  
Depth Of Penetration ◽  
Weld Bead Geometry ◽  
Bead Width ◽  
Input Parameters

Gas Tungsten Arc Welding (GTAW) is one of the most widely used welding process in industry. The input parameters play a very significant role in determining the quality of a welded joint (geometry of weld bead). The joint quality can be evaluated by studying the features of weld bead geometry (output parameters) such as Bead Width (BW), Bead Height (BH) and Depth of Penetration (DP). Present study focused on welding of austenitic stainless steel sheets using GTAW process with 316L material. The output variables are determined according to gas flow rate, travel speed and current. Grey relational analysis is applied to optimize the input parameters simultaneously considering the multiple output variables. Finally, confirmation experiment has been conducted to validate the optimized parameters and found to be correlated.

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