Optimization of turning parameters for surface roughness and tool life based on the Taguchi method

Hasçalik, Ahmet; Çaydaş, Ulaş

doi:10.1007/s00170-007-1147-0

Cited by 172 publications

(64 citation statements)

References 9 publications

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“…The surface roughness was maximum in the uncoated cutting tool because of its rough surface morphology and porous microstructure. [37][38][39][40][41] The responsible factors for the input parameters based on the S=N ratios and the con¯rmation experimental results for the surface roughness are shown in Table 13.…”

Section: Cutting Force Analysismentioning

confidence: 99%

Comparative Evaluation of Performances of TiAlN-, AlCrN- and AlCrN/TiAlN-Coated Carbide Cutting Tools and Uncoated Carbide Cutting Tools on Turning EN24 Alloy Steel

Kumar

Prabu

Kumar³

2017

J. Adv. Manuf. Syst.

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In the present work, the performances of TiAlN-, AlCrN-and AlCrN/TiAlN-coated and uncoated tungsten carbide cutting tool inserts are evaluated from the turning studies conducted on EN24 alloy steel workpiece. The output parameters such as cutting forces, surface roughness and tool wear for TiAlN-, AlCrN-and AlCrN/TiAlN-coated carbide cutting tools are compared with uncoated carbide cutting tools (K10). The design of experiment based on Taguchi's approach is used to obtain the best turning parameters, namely cutting speed (V ), feed rate (f) and depth of cut (d), in order to have a better surface¯nish and minimum tool°ank wear. An orthogonal array (L 9 Þ was used to conduct the experiments. The results show that the AlCrN/ TiAlN-coated cutting tool provided a much better surface¯nish and minimum tool°ank wear. The minimum tool°ank wear and minimum surface roughness were obtained using AlCrN/ TiAlN-coated tools, when V ¼ 160 m/min, f ¼ 0:318 mm/rev and d ¼ 0:3 mm.

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Section: Cutting Force Analysismentioning

confidence: 99%

Comparative Evaluation of Performances of TiAlN-, AlCrN- and AlCrN/TiAlN-Coated Carbide Cutting Tools and Uncoated Carbide Cutting Tools on Turning EN24 Alloy Steel

Kumar

Prabu

Kumar³

2017

J. Adv. Manuf. Syst.

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“…Therefore, the factors instigating variations should be determined and checked under laboratory environments. These studies are counted below the scope of off-line improvement of quality [16]. The Taguchi method usually used to reduce the number of experiments by using orthogonal arrays and also attempts to minimize the effects of control factors.…”

Section: Experimental Designmentioning

confidence: 99%

Statistical and Regression Analysis of Vibration of Carbon Steel Cutting Tool for Turning of EN24 Steel Using Design of Experiments

Aherwar¹,

Unune²,

Pathri³

et al. 2014

IJMECH

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“…(8). The weights were fed back after every iteration till the reduced error rate resulted in an improvement of the output of the training.…”

Section: Ann Modelmentioning

confidence: 99%

“…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%

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.

show abstract

Optimization of turning parameters for surface roughness and tool life based on the Taguchi method

Cited by 172 publications

References 9 publications

Comparative Evaluation of Performances of TiAlN-, AlCrN- and AlCrN/TiAlN-Coated Carbide Cutting Tools and Uncoated Carbide Cutting Tools on Turning EN24 Alloy Steel

Comparative Evaluation of Performances of TiAlN-, AlCrN- and AlCrN/TiAlN-Coated Carbide Cutting Tools and Uncoated Carbide Cutting Tools on Turning EN24 Alloy Steel

Statistical and Regression Analysis of Vibration of Carbon Steel Cutting Tool for Turning of EN24 Steel Using Design of Experiments

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

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