Parametric Evaluation and Optimization of Laser Machining of SS304 Using Response Surface Methodology

Magdum, Vikas B.; Kittur, Jayant K.; Kulkarni, Sachin C.

doi:10.1007/s11665-022-07549-y

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Optimizing machining parameters in laser manufacturing processes requires experimental design, statistical data analysis, mathematical knowledge for formulation, and processspecific knowledge. However, instead of dealing with such difficult processes in which all parameters are modeled, the most dominant parameters are tried to be determined by statistical methods [21][22][23][24][25]. In recent years, metaheuristic algorithms, machine learning methods, and hybrid approches have been used in material processing to optimize laser processes such as cutting, drilling and machining.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Laser Cutting Parameters for PMMA Using Metaheuristic Algorithms

Ürgün,

Yiğit,

Fidan

et al. 2024

Arab J Sci Eng

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This study fixates on determining the optimum laser input parameters that simultaneously meet the desired kerf width and depth during CO2 laser cutting of various polymethylmethacrylate (PMMA) sheets. It has three contributions. The first is to model the cutting process of PMMA by polynomial curve fitting as a function of laser power, laser speed, and standoff distance. R-squared (R2), adjusted R2 and root-mean-square error (RMSE) criteria were taken into account to measure the performance of the proposed model. The effect of laser parameters on the process is investigated by analysis of variance (ANOVA) and sensitivity analysis. The second is to optimize the derived nonlinear regression models using genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and ant lion optimization (ALO) metaheuristic methods and compare the performances of the algorithms. The third is to compare the adequacy of the optimization process with the artificial neural network (ANN). The investigations exhibited that the best-fitting polynomials are obtained with the R2 and adjusted R2 values of over 99% and 97%, respectively. ANOVA and sensitivity test revealed that the sensitivity of the laser power, which is the most effective parameter, was 150 at low powers and decreased to 0 as the power value increased. When the nozzle distance is 4.1, the proposed metaheuristics gave effective and sufficiently accurate results. PSO stood out in terms of both best cost value (3.49 × 10–8) and relative error value (0.19%). The relative error of the ANN was found as 3% in terms of kerf depth.

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Section: Introductionmentioning

confidence: 99%

Optimization of Laser Cutting Parameters for PMMA Using Metaheuristic Algorithms

Ürgün,

Yiğit,

Fidan

et al. 2024

Arab J Sci Eng

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Feasibility analysis and cutting process research on laser cutting medium-thick 20CrNiMo steel plates using a high-power fiber laser without assisted blowing

Liao,

Wang,

Zhong

et al. 2024

Journal of Manufacturing Processes

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Experimental and numerical study of a lateral waterjet laser cutting of an SS304 brush seal

Qin,

Wang

et al. 2024

Opt. Express

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This study explores the application of lateral waterjet laser cutting in the fabrication of brush seals for gas turbine engines, with the aim of optimizing process parameters to enhance cutting quality. Experimental and numerical analyses were conducted to assess the effects of laser power, scanning speed, waterjet velocity, and waterjet standoff distance on key outcomes, such as kerf width, heat-affected zone, and recast layer thickness. The findings indicate that a laser power of 500 W, scanning speed of 15 mm/s, waterjet velocity up to 10 m/s, and waterjet standoff distance of 1 mm are optimal for reducing thermal damage and achieving precise kerfs. Furthermore, numerical simulations highlight the significance of controlled molten flow in realizing these optimal results, providing a comprehensive understanding of the cutting mechanism. These findings position lateral waterjet laser cutting as a better technique for brush seal manufacturing, offering significant improvements over conventional methods.

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Parametric Evaluation and Optimization of Laser Machining of SS304 Using Response Surface Methodology

Cited by 4 publications

References 18 publications

Optimization of Laser Cutting Parameters for PMMA Using Metaheuristic Algorithms

Optimization of Laser Cutting Parameters for PMMA Using Metaheuristic Algorithms

Feasibility analysis and cutting process research on laser cutting medium-thick 20CrNiMo steel plates using a high-power fiber laser without assisted blowing

Experimental and numerical study of a lateral waterjet laser cutting of an SS304 brush seal

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