Optimization of GMAW Process Parameters Using Particle Swarm Optimization

The quality of a welded joint is determined by key attributes such as dilution and the weld bead geometry. Achieving optimal values associated with the above-mentioned attributes of welding is a challenging task. Selecting an appropriate method to derive the parameter optimality is the key focus of this paper. This study analyzes several versatile parametric optimization and prediction models as well as uses statistical and machine learning models for further processing. Statistical methods like grey-based Taguchi optimization is used to optimize the input parameters such as welding current, wire feed rate, welding speed, and contact tip to work distance (CTWD). Advanced features of artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) models are used to predict the values of dilution and the bead geometry obtained during the welding process. The results corresponding to the initial design of the welding process are used as training and testing data for ANN and ANFIS models. The proposed methodology is validated with various experimental results outside as well as inside the initial design. From the observations, the prediction results produced by machine learning models delivered significantly high relevance with the experimental data over the regression analysis.

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“…for obtaining minimum dilution and optimal bead geometry using particle swarm mization [28] and ANN integrated with genetic algorithm [29].…”

Section: Plan Of Investigationmentioning

confidence: 99%

“…They developed an optimization model and a prediction model for characteristics such as bead geometry and Dilution percentage. The research proposed optimal input param-eters for obtaining minimum dilution and optimal bead geometry using particle swarm optimization [28] and ANN integrated with genetic algorithm [29].…”

Section: Introductionmentioning

confidence: 99%

Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

Devaraj

Ziout

Qudeiri

2021

Metals

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“…Yadav et al [15] made parametric optimization tensile strength in GMAW by performing Taguchi method. Sreeraj et al [16] made an optimization study for gas metal arc welding parameters using central composite rotatable design and particle swarm method. Shao et al [17] developed a mathematical model using design of experiments and polynomial regression method for modeling and optimization of gas metal arc welding method.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Taguchi Based Grey Algorithm for Multi Objective Optimization of Gas Metal Arc Welded DP1000 Steel

Karataş¹,

Bayramoğlu

Eşme

et al. 2021

European Mechanical Science

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Euro-6 norms for reduction of CO 2 emissions and ECE-R66-01 regulation for safer passenger transportation became legislative obligations for bus builders. These two adaptations increase the weight of a bus as nearly 600kg which also increases the fuel consumption nearly 1.5-3%. Dual phase (DP) steels can compensate these increases by decreasing the thickness of the components. Because, ferrite provides the steel with good formability and martensite increases the hardness and ultimate tensile strength in DP steels. This study focused on the multi response hybrid optimization of gas metal arc welding (GMAW) of DP1000 steel to determine the optimal parametric combination which gives the maximum ultimate tensile strength (UTS, MPa) and joint efficiency (JE) under minimum heat input (Q, kj/mm). Nine experimental trials which is based on the orthogonal array suggested by Taguchi method was carried out to investigate objective functions for optimization in the experimental range of GMAW parameters such as voltage (U, volt), welding speed (V, mm/min) and wire feed rate (f, m/min). The Taguchi method continued with Grey Relational Analysis (GRA) to overcome the multiobjective optimization model. Finally, Analysis of variance (ANOVA) method has been used to determine the significance of the welding parameters on the responses of UTS, JE and Q. This shows flexible applicability of Taguchi based GRA to find out the effect of each GMAW process parameters onto individual responses.

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“…Patel and Chaudhary (2013) used the Design of Experiment method and grey relational analysis for optimizing the MIG and TIG welding parameters, they also used ANOVA for calculating quality characteristics. Sreeraj et al (2013) presented an experimental study to optimize various input process parameters to found the optimum dilution in stainless steel cladding of low-carbon steel plates which was welded by arc welding. Experiments were performed based on central composite rotatable design with full replication technique and using the multiple regression method with PSO technique to optimize the parameters to get minimal dilution.…”

Section: Introductionmentioning

confidence: 99%

Application of entropy measurement technique in grey based Taguchi method for optimization of MIG welding process parameters

Jatav¹,

Chaturvedi²

2016

Int. J. Eng. Sci. Tech

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We seldom realized that without this form of metal work, many structures would cease to be in existence. A skilled journeymen welder joins metal in such a way that it is not able to be parted unless it is cut. Welding is an absolutely essential technique used in various industries like automotive industry, construction industry as well as in the aviation industry. Hence welding process parameters are required to be optimized for their responses or welding characteristics. This study incorporates entropy measurement technique based on grey Taguchi method to analyze multiple quality characteristic optimization of metal inert gas welding of low carbon steel plates. For this study, four control variables are selected current, voltage, gas flow rate and wire feed rate and analysing their effect on the four quality characteristics ultimate tensile strength, elongation %, bending strength and hardness of the weldments have been investigated in this paper. In order to optimize the multiple quality characteristic of the MIG welding, grey relational analysis coupled with entropy measurement method has been employed. Using entropy measurement method, value of weight corresponding to each quality characteristic has been assigned, so that the importance can be properly determined. Using the theory of grey relational analysis, these have been accumulated to calculate the overall grey relational grade. Signal to Noise ratio (S/N ratio) is applied to find the optimal parameter setting. To determine the contribution of MIG welding process parameters, Analysis of variance (ANOVA) on grey relational grade has been calculated. The confirmatory test also has been done for verifying the results. A foresaid methodology has been found fruitful in the cases where simultaneous optimization of huge number of responses is required.

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Optimization of GMAW Process Parameters Using Particle Swarm Optimization

Cited by 14 publications

References 10 publications

Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

Hybrid Taguchi Based Grey Algorithm for Multi Objective Optimization of Gas Metal Arc Welded DP1000 Steel

Application of entropy measurement technique in grey based Taguchi method for optimization of MIG welding process parameters

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