Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

Madić, Miloš; Radovanović, Miroslav; Gostimirović, Marin

doi:10.5267/j.ijiec.2014.9.003

Cited by 8 publications

(7 citation statements)

References 17 publications

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“…Finally, for the constant laser power of 3.6 kW (Figure 2c), an increase in assist gas pressure as well as cutting speed results in a negligible increase of kerf taper angle values. This is in accordance with the results reported by Madić et al [34]. In that research, it has been reported that for certain levels of laser power the effects of assist gas pressure and cutting speed on the change in kerf taper angle may be negligible.…”

Section: Resultssupporting

confidence: 93%

Mathematical Modelling of the Co2 Laser Cutting Process Using Genetic Programming

et al. 2022

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The development of mathematical models by using experimental data is of great importance for modelling and optimization of the laser cutting process. Motivated by the lack of research regarding the use of genetic programming (GP) for deriving empirical mathematical models that describe the laser cutting process, the present study discusses the application of GP to the development of a kerf taper angle mathematical model. The aim was to quantify the relationship between three selected input parameters (cutting speed, laser power and assist gas pressure) and kerf taper angle using GP in the CO2 laser cutting of aluminium alloy AlMg3. To obtain the experimental database for the GP model evolution process, a laser cutting experiment was planned as per standard full factorial design where all three selected parameters were varied at three levels. The fit between the experimental and the GP model prediction values of kerf taper angle was found to be appropriate. Finally, by using the derived GP mathematical model, the analysis of the effects of input parameters on the change in kerf taper angle values was performed by generating 3D surface plots.

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Section: Resultssupporting

confidence: 93%

Mathematical Modelling of the Co2 Laser Cutting Process Using Genetic Programming

et al. 2022

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“…Due to the ability to find a set of trade-off solutions in a single simulation run, inclination toward the adaptation of AI-based optimization methods especially evolutionary multi-objective optimization (EMO) algorithm shows a growing interest not only to control and predict the behavior of the phenomenon, but also to accomplish a common goal of improving machining performance. Various researchers have employed methods which include statistical and analytical approaches for mathematical modeling (Ciurana et al 2009;Kibria et al 2013;Mishra and Yadava 2013;Madić et al 2015) in order to predict the responses and for multi-response optimization (Dhupal et al 2008;Wang et al 2016;Giorleo et al 2016) in order to control the process parameters during laser micromachining process. Recently, Shivakoti et al (2017) highlighted the use of fuzzy TOPSIS method for the selection of optimal laser micromarking process parameters to improve the marking performance on high strength temperature resistance material such as gallium nitride (GaN).…”

Section: Introductionmentioning

confidence: 99%

Parametric optimization of Nd:YAG laser microgrooving on aluminum oxide using integrated RSM-ANN-GA approach

2018

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Nowadays in highly competitive precision industries, the micromachining of advanced engineering materials is extremely demand as it has extensive application in the fields of automobile, electronic, biomedical and aerospace engineering. The present work addresses the modeling and optimization study on dimensional deviations of square-shaped microgroove in laser micromachining of aluminum oxide (Al 2 O 3) ceramic material with pulsed Nd:YAG laser by considering the air pressure, lamp current, pulse frequency, pulse width and cutting speed as process parameters. Thirty-two sets of laser microgrooving trials based on central composite design (CCD) design of experiments (DOEs) are performed, and response surface method (RSM), artificial neural network (ANN) and genetic algorithm (GA) are subsequently applied for mathematical modeling and multi-response optimization. The performance of the predictive ANN model based on 5-8-8-3 architecture gave the minimum error (MSE = 0.000099) and presented highly promising to confidence with percentage error less than 3% in comparison with experimental result data set. The ANN model combined with GA leads to minimum deviation of upper width, lower width and depth value of − 0.0278 mm, 0.0102 mm and − 0.0308 mm, respectively, corresponding to optimum laser microgrooving process parameters such as 1.2 kgf/cm 2 of air pressure, 19.5 Amp of lamp current, 4 kHz of pulse frequency, 6% of pulse width and 24 mm/s of cutting speed. Finally, the results have been verified by performing a confirmatory test.

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“…Existing studies [7][8][9][10][11][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] do not consider the spatial characteristics of laser beams. Nevertheless, these are accounted for by the FPP, which is one of the key factors of the cutting process.…”

Section: Laser Beam Shape Position and Distortionmentioning

confidence: 99%

“…The details of this approach are described by Orishich et al 12 Only in one study was the FPP used as the input optimization parameter. 28 It is obvious that the beam waist should always be located on the thin-section workpiece's surface (FPP ¼ 0) during cold ablation cutting at extremely high intensities produced by sharp beam focusing. 33 In gas-assisted laser cutting of medium-and thick-section metals, the beam waist rarely coincides with any of the material surfaces of the cutting sheet or melt pool.…”

Section: Laser Beam Shape Position and Distortionmentioning

confidence: 99%

“…Considerable physical complexity justifies the existence of the literature dedicated to empirical investigations of quality parameters. Techniques such as analysis of variances, [17][18][19][20] response surface method, [20][21][22][23] multiple regression analysis, 9,24,25 artificial neural networks, 9,[26][27][28] and genetic algorithms 22 have been used in these studies. Results are captured as polynomials that relate a single output parameter to a set of input parameters.…”

Section: Speed-power-pressure Conceptmentioning

confidence: 99%

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Gas-assisted laser cutting of medium-section metals using spherically aberrated beams

Yurevich

Grimm

Polyakov³

et al. 2015

Opt. Eng

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Abstract. We analyzed the effects of the focal point aberrational offset in optical transport systems for highpower lasers. Transverse and near-axial laser intensity distribution transformations in the presence of both positive and negative spherical aberrations were numerically calculated and experimentally demonstrated for different strengths. We show that spherical aberration yields considerable asymmetry of the focused beam's caustic. Several optical transport systems with identical optical parameters (excluding the noncorrected axial beam spherical aberration) were designed. We examined the effects of the laser intensity profiles produced by these systems on the quality of oxygen-assisted laser cutting of medium-section mild steel. We show that high-quality cuts can be obtained for different shapes of laser intensity distribution. However, the greater the refocusing magnitude introduced by the spherical aberration correction, the more precisely the focal point position must be maintained during the laser cutting process. © The Authors. Published by SPIE under a Creative Commons 1 Introduction Laser cutting of metals and alloys is one of the most commercially valuable laser technology processes. Originating from basic research on laser-matter interaction, 1 the field has significantly developed, now offering a large arsenal of state-of-the-art techniques.2-5 Omitting the energy and resource efficiency issues, 6 the performance of laser cutting can be captured by only two output parameters. These are the cutting speed and kerf quality. Thus, laser cutting optimization reduces to achieving the maximal cutting speed with the best kerf quality. As far as the material separation is concerned, 7 the speed can be dramatically increased. On the other hand, the cutting quality depends on many parameters. The most important and frequently discussed are the kerf geometry (accuracy, width, and taper), the surface quality (cut-edge roughness, striations morphology, and dross and burr inclusions), and the mechanical and metallurgical properties (hardness and strength, heat-affected zone, and oxide layer). 8 The number of parameters dramatically affecting the cutting quality ranges from 25 9 to 75. 4,5 These are nearly evenly divided between the workpiece properties, assist gas parameters, laser machine, and laser beam characteristics. Input-output parameters relationships are mostly nonlinear and exhibit strong interdependence. In this regard, maximal precision is provided by the studies that focus on only one input-output parameter pair. Yet, this "one parameter at one time" approach does not provide complete information on the process as a whole.

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Ann modeling of kerf transfer in Co2 laser cutting and optimization of cutting parameters using monte carlo method

Cited by 8 publications

References 17 publications

Mathematical Modelling of the Co2 Laser Cutting Process Using Genetic Programming

Mathematical Modelling of the Co2 Laser Cutting Process Using Genetic Programming

Parametric optimization of Nd:YAG laser microgrooving on aluminum oxide using integrated RSM-ANN-GA approach

Gas-assisted laser cutting of medium-section metals using spherically aberrated beams

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