Fuzzy rule based approach for predicting weld bead geometry in gas tungsten arc welding

Ghanty, P.; Paul, Satyam; Roy, Anandarup; Mukherjee, D.; Pal, Nikhil R.; Vasudevan, M.; Kumar, Hemant; Bhaduri, A.K.

doi:10.1179/174329308x271751

Cited by 15 publications

(10 citation statements)

References 39 publications

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“…This fuzzy system mainly has the following components: (1) A rule base that has a collection of rules, (2) a database with the help of which the membership functions can be decided and its parameters are to be tuned, and (3) a decision-making mechanism that carries out an inference procedure to map the inputs and the rules. [15,19] The schematic network-like structure used in ANFIS is shown in Figure 3, indicating the essential input and the output parameters.…”

Section: A Development Of Anfis Modelsmentioning

confidence: 99%

Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

Gowtham

Vasudevan

Maduraimuthu

et al. 2011

Metall Mater Trans B

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Modified 9Cr-1Mo ferritic steel is used as a structural material for steam generator components of power plants. Generally, tungsten inert gas (TIG) welding is preferred for welding of these steels in which the depth of penetration achievable during autogenous welding is limited. Therefore, activated flux TIG (A-TIG) welding, a novel welding technique, has been developed in-house to increase the depth of penetration. In modified 9Cr-1Mo steel joints produced by the A-TIG welding process, weld bead width, depth of penetration, and heat-affected zone (HAZ) width play an important role in determining the mechanical properties as well as the performance of the weld joints during service. To obtain the desired weld bead geometry and HAZ width, it becomes important to set the welding process parameters. In this work, adaptative neuro fuzzy inference system is used to develop independent models correlating the welding process parameters like current, voltage, and torch speed with weld bead shape parameters like depth of penetration, bead width, and HAZ width. Then a genetic algorithm is employed to determine the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width.

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Section: A Development Of Anfis Modelsmentioning

confidence: 99%

Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

Gowtham

Vasudevan

Maduraimuthu

et al. 2011

Metall Mater Trans B

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“…The works included eleven input process parameters but only two output parameters. Also, fuzzy rule-based model [16] and genetic algorithm [17] were employed to predict or optimize the geometry of the weld bead. Though the published works can obtain corresponding some relations, they lacked deep interpretation of the weld bead forming rule, especially the influential levels of the different key operational parameters on the key characteristic parameters of weld bead based on theoretical and experimental analyses.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Weld Bead Forming Rule during Double-Pulsed GMAW Process Based on Grey Relational Analysis

et al. 2019

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Weld bead forming rule is very important during double-pulsed gas metal arc welding (DP-GMAW) process, and this process has more advantages than that of conventional arc welding process. This work employed grey rational analysis to explore the weld bead forming rule. Since the latest twinpulse XT DP control process was employed, the parameters adjustment was easier than that of conventional operation. The grey relational analyses between five main process parameters, which were average welding current, welding speed, twin pulse relation, twin pulse frequency together with twin pulse current change in percent, and three key characteristic parameters, which were bead width, bead height and penetration, were conducted to explore the weld bead forming rule. To accurately calculate the grey relational degree, the negative relevancies were transformed to positive ones. According to calculations and corresponding analyses, it can be concluded that the effects of average welding current and welding speed on the weld bead forming and key characteristic parameters of the weld bead were higher than that of other process parameters. Moreover, the relevancies between key characteristic parameters of the weld bead, and process parameters which included twin pulse relation, average welding current and twin pulse current change in percent were positive, while the relevancies between key characteristic parameters and other two process parameters were negative. The work can supply a new method to evaluate the effects of process parameters during the DP-GMAW process on the weld bead forming or other process characteristics, and references for parameters selection and process optimization.

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“…The use of fuzzy rule based systems to model the relationship between weld control parameters and the weld bead geometry features is explored in this paper. The system is tested on three datasets and the performance is found to be satisfactory compared to the multilayer perceptron (MLP) and radial basis function (RBF) neural networks based systems [9]. Simpson S W elucidates the signature image approach to welding fault detection, covering the calculation of signature image data objects from blocks of welding electrical data (voltage and current), the definition of appropriate vector operations, and the manipulation of the signatures to permit detection of welding faults [10].…”

Section: Introductionmentioning

confidence: 99%

Application of Nonlinear Dynamical Methods for Arc Welding Quality Monitoring

Zhou

2016

TELKOMNIKA

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Owing to its diverse, the stability of arc signals in high- IntroductionWith the development and application of automation technology and computer technology, the digitization and intelligent of welding has become a focus of the development of welding technology nowadays.Welding process is a complex physical and chemical process with various interference, and the stability of welding quality control is a more complex and difficult nonlinear problem [1][2][3]. Electrical signals,such as welding current and welding voltage,contains abundant information of welding process.If we can extract the feature information accurately,and then using the scientific method to undertake statistical analysis and calculation,will surely can be better sight into the characteristics of the mechanism of welding process [4,5]. Many scholars have studied in this respect: to reduce the subjectivity of double wire pulsed welding welding process stability evaluation. Considering weld strength as the quality characteristic in the selection of process parameters, fume formation in a pulsed gas metal arc welding (GMAW) process is investigated by coupling a time-dependent axi-symmetric two-dimensional model, which takes into account both droplet detachment and production of metal vapour, with a model for fume formation and transport based on the method of moments for the solution of the aerosol general dynamic equation [6]. Three geometry changes to the inner bore of a welding nozzle and their effects on weld quality during gas metal arc welding (GMAW) were investigated through the use of computational fluid dynamic (CFD) models and experimental trials [7]. An adaptive technique based on estimation of signal parameters via rotational invariance technique (ESPRIT) is proposed that optimizes the accuracy and computation time for harmonic/interharmonic estimation of stationary as well as nonstationary power supply signals [8]. The use of fuzzy rule based systems to model the relationship between weld control parameters and the weld bead geometry features is explored in this paper. The system is tested on three datasets and the performance is found to be satisfactory compared to the multilayer perceptron (MLP) and radial basis function (RBF) neural networks based systems [9]. Simpson S W elucidates the signature image approach to welding fault detection, covering the calculation of signature image data objects from blocks of welding electrical data (voltage and current), the definition of appropriate vector operations, and the manipulation of the signatures to permit detection of welding faults [10].The above analysis of the status of the system is not comprehensive, the extracted characteristic information is more rely on the experience of the people,and overemphasize on the independence of the state. Loss time-varying characteristics of the signal, and ignore the

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Fuzzy rule based approach for predicting weld bead geometry in gas tungsten arc welding

Cited by 15 publications

References 39 publications

Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

Exploration of Weld Bead Forming Rule during Double-Pulsed GMAW Process Based on Grey Relational Analysis

Application of Nonlinear Dynamical Methods for Arc Welding Quality Monitoring

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