Optimization of Process Parameters in Laser Welding of Hastelloy C-276 Using Artificial Neural Network and Genetic Algorithm

Sampreet, K. R.; Mahidhar, Vasareddy; Narayanan, Rengarajan; Kannan, T.

doi:10.1142/s0218625x20500420

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…GA has been successfully implemented in many manufacturing processes to find the best parametric combination. 15 GA is generally used to identify the optimized parameters based on bio-inspired operators such as mutation, crossover and selection. GA works with a set of individuals called a population.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…The GA parameters used in this work are as follows, population size: 100, crossover rate: 0.9, and mutation rate: 0.01. 13,15 Selection was done based on roulette wheel method and single point crossover type was used. Tournament selection, Roulette wheel selection, rank selection, steady state selection is some of the methods available to implement GA. Roulette wheel is one of the selection methods which helps in selecting the individuals for the next generation.…”

Section: Genetic Algorithmmentioning

confidence: 99%

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Experimental studies and optimization of process parameters in laser welding of stainless steel 304 H

Selvaperumal

Thangaraju

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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The prime objective of this work is to find the best combination of parameters in laser welding of stainless-steel 304 H plates. CO2 laser welding was used to join 5 mm thick stainless-steel 304 H plates. Laser power, welding speed, and focal position were used as input parameters, and experiments were conducted on the basis of the Taguchi L27 matrix. The quality of the weld was analysed by measuring depth of penetration, bead width and hardness. The best parameter combination was found using artificial neural network and genetic algorithm. The Levenberg–Marquardt algorithm predicted output responses more accurately. A confirmatory test was carried out for the optimized parameters identified by the genetic algorithm. The percentage error between the experimental and predicted genetic algorithm values was below 6%. In comparison to the base metal, the optimized weld hardness was increased by 8%. The electron backscatter diffraction analysis of the optimized weld revealed the presence of a higher percentage of high-angle grain boundary than low-angle grain boundary.

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Section: Genetic Algorithmmentioning

confidence: 99%

Section: Genetic Algorithmmentioning

confidence: 99%

Experimental studies and optimization of process parameters in laser welding of stainless steel 304 H

Selvaperumal

Thangaraju

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…Even though the method does not guarantee a globally optimal solution, its stochastic nature permits the convergence to a large set of feasible solutions with less computational effort. Previously, the GA has produced excellent results when combined with ANN [31][32][33][34][35][36]. Furthermore, there is still a considerable research gap on approaches that not only combine GA multiobjective optimization and ANN models but also include data acquisition through both experiments and computational simulations for process development of the laser polishing.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of Laser Polishing of Additively Manufactured Ti-6Al-4V Parts for Minimum Surface Roughness and Heat-Affected Zone

et al. 2022

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Metal parts produced by additive manufacturing often require postprocessing to meet the specifications of the final product, which can make the process chain long and complex. Laser post-processes can be a valuable addition to conventional finishing methods. Laser polishing, specifically, is proving to be a great asset in improving the surface quality of parts in a relatively short time. For process development, experimental analysis can be extensive and expensive regarding the time requirement and laboratory facilities, while computational simulations demand the development of numerical models that, once validated, provide valuable tools for parameter optimization. In this work, experiments and simulations are performed based on the design of experiments to assess the effects of the parametric inputs on the resulting surface roughness and heat-affected zone depths. The data obtained are used to create both linear regression and artificial neural network models for each variable. The models with the best performance are then used in a multiobjective genetic algorithm optimization to establish combinations of parameters. The proposed approach successfully identifies an acceptable range of values for the given input parameters (laser power, focal offset, axial feed rate, number of repetitions, and scanning speed) to produce satisfactory values of Ra and HAZ simultaneously.

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Optimization of Process Parameters Affecting Cutting Force, Power Consumption and Surface Roughness Using Taguchi-Based Gray Relational Analysis in Turning Aisi 1040 Steel

Akgün

2021

Surf. Rev. Lett.

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This study focuses on optimization of cutting conditions and modeling of cutting force ([Formula: see text]), power consumption ([Formula: see text]), and surface roughness ([Formula: see text]) in machining AISI 1040 steel using cutting tools with 0.4[Formula: see text]mm and 0.8[Formula: see text]mm nose radius. The turning experiments have been performed in CNC turning machining at three different cutting speeds [Formula: see text] (150, 210 and 270[Formula: see text]m/min), three different feed rates [Formula: see text] (0.12 0.18 and 0.24[Formula: see text]mm/rev), and constant depth of cut (1[Formula: see text]mm) according to Taguchi L18 orthogonal array. Kistler 9257A type dynamometer and equipment’s have been used in measuring the main cutting force ([Formula: see text]) in turning experiments. Taguchi-based gray relational analysis (GRA) was also applied to simultaneously optimize the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]). Moreover, analysis of variance (ANOVA) has been performed to determine the effect levels of the turning parameters on [Formula: see text], [Formula: see text] and [Formula: see text]. Then, the mathematical models for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) have been developed using linear and quadratic regression models. The analysis results indicate that the feed rate is the most important factor affecting [Formula: see text] and [Formula: see text], whereas the cutting speed is the most important factor affecting [Formula: see text]. Moreover, the validation tests indicate that the system optimization for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) is successfully completed with the Taguchi method at a significance level of 95%.

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Optimization of Process Parameters in Laser Welding of Hastelloy C-276 Using Artificial Neural Network and Genetic Algorithm

Cited by 4 publications

References 17 publications

Experimental studies and optimization of process parameters in laser welding of stainless steel 304 H

Experimental studies and optimization of process parameters in laser welding of stainless steel 304 H

Multiobjective Optimization of Laser Polishing of Additively Manufactured Ti-6Al-4V Parts for Minimum Surface Roughness and Heat-Affected Zone

Optimization of Process Parameters Affecting Cutting Force, Power Consumption and Surface Roughness Using Taguchi-Based Gray Relational Analysis in Turning Aisi 1040 Steel

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