A genetic algorithmic approach for optimization of surface roughness prediction model

Suresh, P.; Rao, P. Venkateswara; Deshmukh, S.G.

doi:10.1016/s0890-6955(02)00005-6

Cited by 295 publications

(149 citation statements)

References 17 publications

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“…Similar finding was observed for turning gray cast iron [13] , which is not the case for the present work. Suresh et al [18] studied a genetic algorithmic approach for optimizing the surface finish prediction model for cutting carbon steel. This approach gives the minimum and maximum values of surface roughness and their respective optimal machining conditions.…”

Section: First-order Modelmentioning

confidence: 99%

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Surface Roughness Prediction Model in Machining of Carbon Steel by PVD Coated Cutting Tools

Şahin¹,

Motorcu²

2004

American J. of Applied Sciences

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Abstract:The surface roughness model in the turning of AISI 1040 carbon steel was developed in terms of cutting speed, feed rate and depth of cut using response surface methodology. Machining tests were carried out using PVD-coated tools under different cutting conditions. The surface roughness equations of cutting tools when machining the carbon steels were achieved by using the experimental data. The results are presented in terms of mean values and confidence levels. The established equation shows that the feed rate was found to be a main influencing factor on the surface roughness. It increased with increasing the feed rate, but decreased with increasing the cutting speed and the depth of cut, respectively. The variance analysis for the second-order model shows that the interaction terms and the square terms were statically insignificant. However, it could be seen that the first-order effect of feed rate was significant while cutting speed and depth of cut was insignificant. The predicted surface roughness of the samples was found to lie close to that of the experimentally observed ones with 95% confident intervals.

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Section: First-order Modelmentioning

confidence: 99%

“…This method has been used some by some other researcher. However, a little work on machining of steels has given to the analysis and prediction of tool life [4][5][6][7][8] and surface roughness [9][10][11][12][13][14][15][16][17][18][19][20][21][22] .…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Prediction Model in Machining of Carbon Steel by PVD Coated Cutting Tools

Şahin¹,

Motorcu²

2004

American J. of Applied Sciences

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“…The results showed the optimum parameters that give minimum surface roughness. Suresh et al [7] proposed a prediction model for surface roughness after turning operation of mild steel. The response surface methodology was used to determine the factors affecting the process.…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Investigation and Stress Modeling by Finite Element on Orthogonal Cutting of Copper

Zein

Irfan

2018

Metals

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Abstract:In this paper, a modern non-contacting optical technique was used to study the surface roughness of commercially pure copper. Finite element (FE) method was applied to predict the stress during orthogonal cutting by simulating the machining process. The experimental work empathized mainly on the effect of cutting speed (N) and feed rate (f) on the surface roughness of copper. Scanning electron microscope (SEM) was utilized to evaluate the surface variations at different machining conditions. Johnson-Cook mathematical model was adopted and employed to determine the parameters of the material. Furthermore, the maximum Von-Mises stress was predicted as a function of machining conditions. A software package of code (ABAQUS/CAE) was used for the analysis and response surface methodology (RSM) was applied to visualize the results. The results showed a significant effect of the feed rate/cutting speed interaction on surface roughness and Von-Mises stress of copper. An enhancement of 14% in surface roughness was perceived with increasing the cutting speed. A good agreement was observed between experimental and analytical results.

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“…Surface roughness is mostly based on the cutting parameters (cutting speed, feed rate, and depth of cut) and sometimes some other parameters [4]. In the literature, various models for the optimum surface roughness have been reported [5][6][7][8][9][10]. The feed rate was found to be the dominant factor among the cutting parameters (cutting speed, feed rate and cutting time) on the surface roughness and flank wear during the turning of AISI H11, using the response surface methodology (RSM) [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…regression analysis, genetic algorithm, ANN, Taguchi method etc. A genetic algorithm approach was used for prediction of the surface roughness of mild steel in a turning operation [5,15]. Surface roughness during the turning of free machining steel [16] and AISI 4340 [17] was studied using ANN [16,18,19] and multiple regression [17], and it was found that the feed rate had the dominant influence, among the cutting parameters, on the surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network and regression-based models for prediction of surface roughness during turning of red brass (C23000)

Hanief¹,

Wàňi²

2016

J. Mech, Eng, Sci.

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In this study, models based on artificial neural networks (ANN) and regression analysis were developed to predict the surface roughness during the turning of red brass (C23000), using a high-speed steel (HSS) tool. The full factorial design approach was used for experimentation to achieve a high level of confidence. The influence of cutting parameters (cutting speed, feed rate and depth of cut) on the surface roughness was also investigated using ANOVA. The ANN model having hyperbolic tangent sigmoid (tansig) and linear (purelin) transfer functions was used for the hidden and output layers respectively. The regression model based on the power law was also developed. It was found that at a speed of 840 m/sec and depth of cut of 0.1 mm, the surface roughness changed by 11.3% when changing the feed rate by 5.6%. However, the surface roughness changed by only 6.8% when changing the velocity by 6% at the feed rate of 0.12 mm and depth of cut 0.1 mm. A similar trend was observed for different feed rates, speeds and depths of cut. It was concluded that the feed rate was the most significant factor influencing the surface roughness, followed by the depth of cut and cutting speed. The models developed were compared using statistical methods: coefficient of determination (R 2 ) and mean absolute percentage error (MAPE). R 2 was found to be 0.99784 and 0.9969 for the ANN and regression models, respectively. Similarly, MAPE was found to be 1.4243% and 4.8161% for the ANN and regression models respectively. It was concluded that the ANN model is a superior choice over the multiple regression model for the prediction of surface roughness. The accuracy of the ANN can be attributed to its ability to capture the nonlinearities involved in the turning operation.

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A genetic algorithmic approach for optimization of surface roughness prediction model

Cited by 295 publications

References 17 publications

Surface Roughness Prediction Model in Machining of Carbon Steel by PVD Coated Cutting Tools

Surface Roughness Prediction Model in Machining of Carbon Steel by PVD Coated Cutting Tools

Surface Roughness Investigation and Stress Modeling by Finite Element on Orthogonal Cutting of Copper

Artificial neural network and regression-based models for prediction of surface roughness during turning of red brass (C23000)

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