Optimization of activated TIG welding parameters for improving weld joint strength of AISI 4135 PM steel by genetic algorithm and simulated annealing

Joseph, Joby; Muthukumaran, S.

doi:10.1007/s00170-015-7599-8

Cited by 26 publications

(6 citation statements)

References 15 publications

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“…For the third technique, a simulated annealing (SA) algorithm -and sometimes adaptive simulated annealing -was widely used to search the optimal process parameters of many welding processes such as SAW, friction stir welding, TIG welding, amongst others [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Influence of Welding Parameters on Optimization of the Tensile Strength and Peak Temperature in AISI 1020 Alloy Joints Welded by SAW

Jassim

Lieth

Al-Sabur

et al. 2022

Preprint

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Submerged Arc Welding (SAW) is a safe and efficient process for joining thick plates AISI 1020. A high-quality weld joint is a critical objective in a series of optimization studies. The current study focuses on maximizing the ultimate tensile strength and minimizing the peak temperature using Taguchi, Genetic Algorithm (GA), and Simulated Annealing (SA) algorithms. The input parameters in the three techniques were voltage (V), welding speed (S), and wire feed rate (F). At a 95% confidence level, the regression models were combined using the ANOVA to predict tensile strength and peak temperature. The maximum ultimate tensile strength was 599MPa achieved at a welding speed of 30 mm/s, an arc voltage of 30 V, and a wire feed rate of 120 mm/s, while the minimal peak temperature was 417C under the same conditions. With the increase of the welding parameters (welding speed, arc voltage, and feed rate), the ultimate tensile strength was increased. Furthermore, the average hardness achieved was 250 at welding metal and 292 at HAZ, while it was 275 at the base metal. The results were supported with an examination of microstructure. In the heat-affected zone (HAZ), the grain was finer while its grain size was larger in specimens with high tensile strength. It was noticed that the HAZ contains pearlite and some colonies of ferrite.

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Section: Introductionmentioning

confidence: 99%

Influence of Welding Parameters on Optimization of the Tensile Strength and Peak Temperature in AISI 1020 Alloy Joints Welded by SAW

Jassim

Lieth

Al-Sabur

et al. 2022

Preprint

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“…Chang et al [24] developed a backpropagation ANN model for predicting the penetration morphology of asymmetrical fillet welds and used a mind evolutionary algorithm to optimize the model. Joseph and Muthukumaran [25] used a genetic algorithm and simulated annealing technique to determine the optimal process parameters for activated tungsten inert gas. Sudhakaran and Sakthivel [26] developed neural network models for predicting bead parameters in the GTAW process, such as depth of penetration, bead width, and depth to width ratio.…”

Section: Introductionmentioning

confidence: 99%

Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm

Tomaz

Colaço

Sarfraz

et al. 2021

Int J Adv Manuf Technol

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Gas tungsten arc welding (GTAW) technology is widely used in industry and has advantages, including high precision, excellent welding quality, and low equipment cost. However, the inclusion of a large number of process parameters hinders its application on a wider scale. Therefore, there is a need to implement the prediction and optimization models that effectively enhance the process performance of the GTAW process in different applications. In this study, a five-factor five-level central composite design (CCD) matrix was used to conduct GTAW experiments. AISI 1020 steel blank was used as a substrate; UTP AF Ledurit 60 and UTP AF Ledurit 68 were used as the materials of two tubular wires. Further, an artificial neural network (ANN) was used to simulate the GTAW process and then combined with a genetic algorithm (GA) to determine welding parameters that can provide an optimal weld. In welding experiments, five different welding current levels, welding speed, distance to the nozzle, angle of movement, and frequency of the wire feed pulses were used. Using GA, optimal welding parameters were determined: welding current = 222 A, welding speed = 25 cm/min, nozzle deflection distance = 8 mm, travel angle = 25°, wire feed pulse frequency = 8 Hz. The determination coefficient (R2) and RMSE value of all response parameters are satisfactory, and the R2 of all the data remained higher than 0.65.

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“…The intelligent optimization algorithm has also been effectively applied to the optimization of process parameters. Genetic algorithm [4,5] and simulated annealing algorithm [6,7] take the output of the prediction model as the fitness function. Based on the rule algorithm, the optimal combination of process parameters can be solved through repeated iterations to meet the required fitness requirements.…”

Section: Introductionmentioning

confidence: 99%

Machining-Reliability-based Optimization of Process Parameters for Marine Diesel Engine Block Hole System Using HJ-PSO

Zhou¹,

Yang²,

Sun³

et al. 2021

Preprint

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The processing quality of the block hole system affects the working performance of the marine diesel engine block directly. Choosing an appropriate combination of process parameters is a prerequisite to improve the accuracy of the block hole system. Uncertain fluctuations of process parameters during the machining process would affect the process reliability of the block hole system, resulting in an ultra-poor accuracy. For this reason, the RBF method is used to establish the relationship between the verticality of the cylinder hole and process parameters, including cutting speed, depth of cut, and feed rate. Taking the minimum cylinder hole verticality as the goal and setting the process reliability constraints of the cylinder hole based on Monte Carlo, a reliability optimization model of processing parameters for cylinder hole is established in this paper. Meanwhile, an improved particle swarm algorithm was designed to solve the model, and eventually, the global optimal combination of process parameters for the cylinder hole processing of the diesel engine block in the reliability stable region was obtained.

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Optimization of activated TIG welding parameters for improving weld joint strength of AISI 4135 PM steel by genetic algorithm and simulated annealing

Cited by 26 publications

References 15 publications

Influence of Welding Parameters on Optimization of the Tensile Strength and Peak Temperature in AISI 1020 Alloy Joints Welded by SAW

Influence of Welding Parameters on Optimization of the Tensile Strength and Peak Temperature in AISI 1020 Alloy Joints Welded by SAW

Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm

Machining-Reliability-based Optimization of Process Parameters for Marine Diesel Engine Block Hole System Using HJ-PSO

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