Application of response surface methodology on investigating flank wear in machining hardened steel using PVD TiN coated mixed ceramic insert

Sahoo, Ashok Kumar; Orra, Kashfull; Routra, B.C.

doi:10.5267/j.ijiec.2013.07.001

Cited by 17 publications

(8 citation statements)

References 12 publications

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“…They concluded that the developed relationship can be effectively used to predict flank wear of carbide tool at a confidence level of 95%. Sahoo et al [6], confirmed this conclusion when studying the development of flank wear model in turning hardened EN 24 steel with PVD TiN coated mixed ceramic insert under dry environment. …”

supporting

confidence: 67%

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

Tebassi¹,

Yallese²,

Meddour³

et al. 2017

Period. Polytech. Mech. Eng.

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IntroductionThe Inconel 718 is one of the most important materials used in modern industries. In addition of the best properties in terms of high strength, corrosion resistance, heat resistance and fatigue resistance, the Inconel 718 has, also a low thermal conductivity as it is mentioned by Lynch [1]. Certainly, this type of alloy is difficult to machine for the following reasons as it is presented by Alauddin [2]: High work hardening rates at machining, strain rates leading to high cutting forces; abrasiveness; toughness, gummy and strong tendency to weld to the tool with forming the built-up edge; low thermal properties leading to high cutting temperatures. However, it has a wide variety of applications such as aircraft gas turbines stack gas reheaters, reciprocating engines and others.In order to respond to the requirements of those applications, it is very important to forecasting surface roughness and cutting force. Consequently, it is necessary to search the best modeling approach of these output parameters. To obtain this objective, several approaches can be used as well as response surface methodology (RSM) and artificial neural network (ANN). Response surface methodology (RSM) is considered as a quick and useful procedure for the investigation and optimization of complex processes as well as modeling machining output parameters. Certainly, Davoodi and Eskandari [3] found that response surface methodology represents a better approach to predict tool life and productivity when turning of N-155 iron-nickel-base superalloy. Shihab et al. [4], investigated cutting temperature during hard turning of AISI 52100 alloy steel using multilayer coated carbide insert; they concluded that the developed RSM model is able to predict cutting temperature for different combination of input parameters very close to experimental values. Arokiadass et al. [5], used response surface methodology to modeling tool flank wear when end milling of LM 25 Al/SiCp with carbide tool. They concluded that the developed relationship can be effectively used to predict flank wear of carbide tool at a confidence level of 95%. Sahoo et al. [6], confirmed this conclusion when studying the development of flank wear model in turning hardened EN 24 steel with PVD TiN coated mixed ceramic insert under dry environment.

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supporting

confidence: 67%

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

Tebassi¹,

Yallese²,

Meddour³

et al. 2017

Period. Polytech. Mech. Eng.

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“…RSM model can be effectively utilized to predict the surface roughness in turning D2 steel. Sahoo et al (2013) developed flank wear model in turning hardened EN 24 steel with PVD TiN coated mixed ceramic insert under dry environment. Machining time has been found to be the most significant parameter on flank wear followed by cutting speed and feed as observed from main effect plot and ANOVA study.…”

Section: Review Of Literature and Objectivesmentioning

confidence: 99%

A response surface methodology and desirability approach for predictive modeling and optimization of cutting temperature in machining hardened steel

Sahoo¹,

Mishra²

2014

10.5267/j.ijiec

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This paper presents an experimental investigation on cutting temperature during hard turning of EN 24 steel (50 HRC) using TiN coated carbide insert under dry environment. The prediction model is developed using response surface methodology and optimization of process parameter is performed by desirability approach. A stiff rise in cutting temperature is noticed when feed and cutting speed are elevated. The effect of depth of cut on cutting temperature is not that much significant compared with cutting speed and feed as observed from main effects plot. The response surface second order model presented high correlation coefficient (R 2 = 0.992) explaining 99.2 % of the variability in the cutting temperature which indicates the goodness of fit for the model to the actual data and high statistical significance of the model. The experimental and predicted values are very close to each other. The calculated error for cutting temperature lies between 1.88-3.19 % during confirmation trial. Therefore, the developed second order model correlates the relationship of the cutting temperature with the process parameters with good degree of approximation. The optimal combination for process parameter is depth of cut at 0.2mm, feed of 0.1597 mm/rev and cutting speed of 70m/min. Based on these combination, the value of cutting temperature is 302.95 0 C whose desirability is one.

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“…The lower surface roughness value indicates that the machining forces are lesser during the trials and the smooth machining of EN24 alloy steel workpiece is obtained. 24,25 Surface¯nish is a signi¯cant index of machinability. The surface conditions of the machined component depend on its service life.…”

Section: Surface Roughness 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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Application of response surface methodology on investigating flank wear in machining hardened steel using PVD TiN coated mixed ceramic insert

Cited by 17 publications

References 12 publications

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

On the Modeling of Surface Roughness and Cutting Force when Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit

A response surface methodology and desirability approach for predictive modeling and optimization of cutting temperature in machining hardened 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

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