Application of RCGA-ANN approach for modeling kerf width and surface roughness in CO2 laser cutting of mild steel

Madić, Miloš; Radovanović, Miroslav

doi:10.1007/s40430-013-0008-z

Cited by 24 publications

(17 citation statements)

References 23 publications

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“…The determine the number of hidden neurons the following equation was considered (Madić & Radovanović, 2013):…”

Section: Ann Architecture and Trainingmentioning

confidence: 99%

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

Madić¹,

Radovanović²,

Gostimirović³

2015

10.5267/j.ijiec

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In this paper, an attempt has been made to develop a mathematical model in order to study the relationship between laser cutting parameters such as laser power, cutting speed, assist gas pressure and focus position, and kerf taper angle obtained in CO 2 laser cutting of AISI 304 stainless steel. To this aim, a single hidden layer artificial neural network (ANN) trained with gradient descent with momentum algorithm was used. To obtain an experimental database for the ANN training, laser cutting experiment was planned as per Taguchi's L 27 orthogonal array with three levels for each of the cutting parameters. Statistically assessed as adequate, ANN model was then used to investigate the effect of the laser cutting parameters on the kerf taper angle by generating 2D and 3D plots. It was observed that the kerf taper angle was highly sensitive to the selected laser cutting parameters, as well as their interactions. In addition to modeling, by applying the Monte Carlo method on the developed kerf taper angle ANN model, the near optimal laser cutting parameter settings, which minimize kerf taper angle, were determined.

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“…The determine the number of hidden neurons the following equation was considered (Madić & Radovanović, 2013):…”

Section: Ann Architecture and Trainingmentioning

confidence: 99%

“…The wide spectrum of industrial application of the laser cutting is due to its convenience of operation, high precision, small heat-affected zone (HAZ), minimum deformity, low cost, high product quality, high cutting speed, low level of noise, flexibility, ease of automation, etc. (Madić & Radovanović, 2013).…”

Section: Introductionmentioning

confidence: 99%

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

Madić¹,

Radovanović²,

Gostimirović³

2015

10.5267/j.ijiec

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“…Although higher assist gas pressure can increase to some degree maximal cutting speed, the cut surface quality is more irregular than when using lower assist gas pressure. Regarding surface roughness of the cut surface, in previous investigation [21], it was observed that there is a nonlinear increase of surface roughness from 1.4 to 2.8 µm, when the assist gas pressure is increased from 3 to 7 bar. It has to be noted that this functional dependence follows the same trend apart from the values of the laser power.…”

Section: Data Analysis Results and Discussionmentioning

confidence: 91%

Fuzzy Logic Approach for the Prediction of Dross Formation in CO2 Laser Cutting of Mild Steel

Madić¹,

Radovanović²,

Ćojbašić³

et al. 2015

JESTR

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Dross free laser cutting is very important in the application of laser cutting technology. This paper focuses on the development of a fuzzy logic model to predict dross formation in CO2 laser oxygen cutting of mild steel. Laser cutting experiment, conducted according to Taguchi's experimental design using L25 orthogonal array, provided a set of data for the development of a fuzzy rule base. The predicting fuzzy logic model is based on using Mamdani-type inference system. Developed fuzzy logic model considered the cutting speed, laser power and assist gas pressure as inputs. Using this model the effects of the selected laser cutting parameters on the dross formation were investigated. Additionally, 3-D surface plots were generated to study the interaction effects of the laser cutting parameters. The analysis revealed that the cutting speed has the most significant effect, followed by laser power and assist gas pressure. The results indicated that the fuzzy logic modeling approach can be effectively used for the dross formation prediction in CO2 laser cutting of mild steel.

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“…However, the increase in the laser beam power causes an increase of the cut surface temperature which leads to an increase of the area of the fusion zone (the beam-metal interaction zone). During the cooling, recast layer is formed and affects the surface roughness [10,11,12].…”

Section: Effect Of Laser Cutting Variables On the Average Surface Roumentioning

confidence: 99%

An Investigation Into the Effect of Co2 Laser Cutting Variables on Cutting Edge Quality of Stainless Steel 316 Sheets

Elwardany¹,

Sonbol²,

Mahdy³

2018

The International Conference on Applied Mechanics and Mechanica

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Laser beam cutting is one of the major applications of lasers in sheet metal working. In this investigation, an experimental study in CO 2 laser cutting process is presented. The aim of this research is to investigate the effect of the laser cutting process variables on the cutting-edge quality parameters. A sheet of stainless steel with a standard grade of 316, 2 mm thickness was chosen as a workpiece material. Several experiments were conducted to investigate the influence of four input variables: laser power (P), traverse speed (v), assist gas pressure (p) and focal plane position (F) on the three most important performance parameters, namely: upper kerf width (UKW), lower kerf width (LKW), and the average surface roughness (R a ). Minitab software was used to determine the main effects of the process variables on the performance parameters. Signal to noise ratio analysis (SN) was used to determine the optimum process variables in their operating rage. This investigation would provide a good demonstration for the most significant input variables on the cutting-edge quality parameters, which will be used for solving related industrial problems. KEYWORDSLaser beam cutting, stainless steel 316, Average surface roughness, Upper kerf width, Lower kerf width.---Fig. 3. Plot of the effect of the different parameters on the surface roughness R a , (The mean value = 0.802, Standard Deviation = 0.354). 132 PT

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Application of RCGA-ANN approach for modeling kerf width and surface roughness in CO2 laser cutting of mild steel

Cited by 24 publications

References 23 publications

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

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

Fuzzy Logic Approach for the Prediction of Dross Formation in CO2 Laser Cutting of Mild Steel

An Investigation Into the Effect of Co2 Laser Cutting Variables on Cutting Edge Quality of Stainless Steel 316 Sheets

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