Selection of optimal machining parameters in pulsed CO2 laser cutting of Al6061/Al2O3 composite using Taguchi-based response surface methodology (T-RSM)

Adalarasan, R.; Santhanakumar, M.; Sathanathan, Thileepan

doi:10.1007/s00170-016-8978-5

Cited by 28 publications

(8 citation statements)

References 24 publications

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“…The interdependences between the Nd:YAG laser cutting control factors and the surface roughness and kerf geometry were investigated for the alloy Al 6061T6 [15]. The same responses were also analyzed for the pulsed CO 2 laser cutting in processing the composite material Al6061/Al2O3 [16]. Sharma and Yadava [17] showed that the use of oxygen as the assisting gas in the laser cutting of a nickel alloy could promote a smooth cut surface.…”

Section: Literature Reviewmentioning

confidence: 99%

Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach

Šibalija

Petronić²,

Milovanović

2019

Metals

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This paper presents an experimental study carried out on Nimonic 263 alloy sheets to determine the optimal combination of laser cutting control factors (assisted gas pressure, beam focus position, laser power, and cutting speed), with respect to multiple characteristics of the cut area. With the aim of designing laser cutting parameters that satisfy the specifications of multiple responses, an advanced multiresponse optimization methodology was used. After the processing of experimental data to develop the process measure using statistical methods, the functional relationship between cutting parameters and the process measure was determined by artificial neural networks (ANNs).Using the trained ANN model, particle swarm optimization (PSO) was employed to find the optimal values of laser cutting parameters. Since the effectiveness of PSO could be affected by its parameter tuning, the settings of PSO algorithm-specific parameters were analyzed in detail. The optimal laser cutting parameters proposed by PSO were implemented in the validation run, showing the superior cut characteristics produced by the optimized parameters and proving the efficacy of the suggested approach in practice. In particular, it is demonstrated that the quality of the Nimonic 263 cut area and the microstructure were significantly improved, as well as the mechanical characteristics.Parameters of the laser cutting process must be properly tuned in order to obtain the desired outputs. The laser power must be sufficient to enable the cutting; the cutting speed should be high enough to prevent the diffusion of heat in a material and to form a broad heat-affected zone (HAZ) [4]. The assisting gas is very important for producing a high-quality cut without a grate, since it does not permit molten material droplets' solidification onto the cut surface. The cutting speed must be balanced; if excessive speed is used the grate would remain, while, with a very low speed, the edge quality of the cut would be affected and a wide HAZ would form [5].The cutting control factors considered in this study are the assisting gas pressure, the position of the beam focus, the laser power, and the cutting speed. At the output, seven responses of the cut area are observed. Since the process is characterized by multiple parameters and responses, an advanced optimization methodology is needed to obtain the optimal cutting setting that satisfies the specifications for multiple, correlated responses. Simulated annealing (SA) and a genetic algorithm (GA) have been employed previously, and it has been demonstrated that SA outperformed GA in the proposed method [6]. Particle swarm optimization (PSO) is used in this study and benchmarked with SA in terms of the quality, i.e., the accuracy of the obtained optimum, the effects of the algorithm's parameters on the obtained optimum, and the convergence speed. Since metaheuristic algorithms must be adequately tuned to obtain the right solution, the setting of the major PSO algorithm's parameters is studied in detail to evaluate th...

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Section: Literature Reviewmentioning

confidence: 99%

Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach

Šibalija

Petronić²,

Milovanović

2019

Metals

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“…Generally, the UC increases as LP is augmented and this may be explained in terms of heat generated during the process. The increase of the LP means an increase in the penetration and the effective energy of the laser beam, consequently, the removal rate of material increases from the groove's surface [6,32]. Figure 16 shows the effect of AGP on UC at different levels of FR and RS respectively.…”

Section: Effect Of Process Conditions On the Groove's Ucmentioning

confidence: 99%

“…This effect can be obtained in reasons of the energy of laser beam melts the exposed surface and the gas jet pushes away the machining products. There is an interaction effect between AGP and the FR [6,32]. Figure 17 illustrates the impact of FR on UC at various levels of RS.…”

Section: Effect Of Process Conditions On the Groove's Ucmentioning

confidence: 99%

Multi-response optimization and modelling of CO2 Laser turning process with (X10CrNiTi18-9) St.St assisted by O2 gas

faruk

rezek

Marzouk

et al. 2022

ERJ. Engineering Research Journal

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The current study investigates the behavior of the grooving process of stainless steel X10CrNiTi18-9 that produced by CO 2 laser turning process. The response surface methodology is adopted to explore this behavior, and conclude the effect of the laser control working conditions on the different quality aspects of the machined groove. The input variables of the study include Laser power (LP), assistance gas (oxygen) pressure (AGP), feed rate (FR) and motor rotating speed (RS). While the metal removal rate (MRR), depth of cut (DC), upper cut width (UC), lower cut width (LC) and root roundness error (RE), are considered as the responses. Using ANOVA analysis, the critical parameters and the associated levels are identified for the optimal combination. The results are modeled and illustrated to closely understand the effect and surface quality of CO 2 -Laser turning process. Results indicate that, MRR, DC, and UC increase with increasing LP and AGP, but they decrease with the increase of FR and RS. There is an interaction effect between (LP and FR) and (LP and RS) on each of LC and RE. The optimal combination of machining conditions reached at LP=3000 watt, AGP=0.6 bar, FR= 200 mm/min, RS= 30 rpm. which maximize MRR (0.5095 g/min), DC (0.6870 mm), UC (0.7028), LC (0.3057) and minimize RE to 16.3230 µm. The optimal conditions were validated laboratory. A small deviate between the optimum experimental results and the predicted values were noticed for MRR (1.8%), DC (2.3%), UC (7.3%), LC (9.2%), and RE (3.2%).

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“…Anamal Hossain et al, [3] used the Mamdani Fuzzy Model to predict kerf width during laser beam machining of PMMA and obtained good results correlating with experimental data. Adalarasan et al [4], used Taguchi to optimize the roughness and kerf width of the aluminium composite during laser cutting. Arun Kumar Pandey and Avanish Kumar Dubey [5] proposed hybrid neural network with genetic algorithm approach for modeling and optimization of the surface roughness and kerf taper during Nd: YAG laser cutting of titanium alloy sheet.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Regression and Neural Network Modeling of Roughness and Kerf Width in CO2 Laser Cutting of Aluminium

2021

Teh. vjesn.

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Laser cutting is the most promising thermal-based unconventional manufacturing process which can cut complex shapes on different materials. Surface roughness and kerf width are the important characteristics that determine the product quality and rely on the rational selection of the input parameters. The present work focuses on comparing surface roughness and the kerf width predicted using regression and artificial neural network model intended for cutting aluminium by CO2 laser. The independent parameters like laser power, assist gas pressure and cutting speed are varied up to three levels and the proposed Box-Behnken design constitutes 17 experiment runs for data acquisition and further modeling. The coefficient of correlation and the absolute mean error percentage are used for the study and comparison of regression and artificial network models. The artificial neural network has a lower mean absolute percentage error (MAPE) than the regression models. In addition, the R-value of the artificial neural network is greater than those of the regression models. The regression modeling methodology has been shown to be inadequate in predicting desired parameters while more reliable results have been obtained with the use of artificial neural network.

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Selection of optimal machining parameters in pulsed CO2 laser cutting of Al6061/Al2O3 composite using Taguchi-based response surface methodology (T-RSM)

Cited by 28 publications

References 24 publications

Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach

Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach

Multi-response optimization and modelling of CO2 Laser turning process with (X10CrNiTi18-9) St.St assisted by O2 gas

Comparative Regression and Neural Network Modeling of Roughness and Kerf Width in CO2 Laser Cutting of Aluminium

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