RSM based Study of Cutting Temperature During Hard Turning with Multilayer Coated Carbide Insert

Shihab, Suha K.; Khan, Zahid A.; Mohammad, Aas; Siddiquee, Arshad Noor

doi:10.1016/j.mspro.2014.07.197

Cited by 36 publications

(20 citation statements)

References 11 publications

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“…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.…”

Section: Introductionmentioning

confidence: 95%

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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Section: Introductionmentioning

confidence: 95%

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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“…As stated, removing these terms of Rt, the new R 2 adj = 82.00% and the lack-of-fit = 0.021. The lack-of-fit Pvalue indicates that there is 17.2% chance that a 'lack of fit F-value' of 2.78 could occur due to noise, as show in Table 9 (Shihab, Khan, Mohammad, & Siddiqueed, 2014b).…”

Section: Resultsmentioning

confidence: 99%

Optimizing production in machining of hardened steels using response surface methodology

et al. 2019

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This paper presents the modeling of tool life and surface roughness for machining AISI 52100 steel with a hardness of 50 HRC through Design of Experiments and Response Surface Methodology (RSM) with a view to enhance the quality and productivity. Knowing that the tool life and surface roughness are factors that influence the quality of the product, this study used the statistical tool of RSM in the search of factors that better determine optimal models. The models obtained prioritize the product quality and the cutting productivity. Results from Analysis of Variance demonstrated that the mathematical models elaborated allowed the prediction of surface roughness parameters' values and tool life (T) with a precision of 95% confidence interval and a coefficient of determination above 94%. The wiper geometry of the tool led to the achievement of low average surface roughness (Ra) ranging from 0.2 to 0.4 μm with relatively high advances (0.2-0.4 mm rev-1) and maximum height of the profile surface roughness (Rt) in the range of 1.4 to 2.8 μm, without making use of the cutting fluid.

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“…Shihab et al [18] used a multi-layer coated carbide insert in hard-turn AISI52100 hardened steel to study the effects of different cutting parameters ( v c , f , a p ) on cutting performance. Bouchelaghem et al [19] investigated the effects of cutting parameters on the hard turning of AISID3 (60 HRC) using the CBN tool. Their results showed that an increase in the value of the cutting parameters results in an increase in cutting temperature.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Tool Rake Surface Geometry on the Hard Turning Process of AISI52100 Hardened Steel

Zhou

et al. 2019

Materials

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The hard turning process has been widely used in the field of hard material precision machining because of its high efficiency, low processing residual stress, and low environmental pollution. Due to its undesirably processing quality, it is still not a substitute for traditional grinding, so many studies have reported that the process has been optimized. However, there has been little research on the geometry optimization of hard cutting tools, which have a great influence on the traditional machining process. In this paper, two tools with different rake face shapes are designed. The finite element analysis method is used to compare their performance with a conventional plane tool while turning hardened steel. The results show that the cutting performance of the designed tool T1 and T2 (chip morphology, cutting force, and cutting temperature) and the quality of the machined surface are improved compared with the tool. The cutting force decreased by 12.72% and 14.74%, the cutting temperature decreased by 7.56% and 9.01%, respectively, and the surface residual stress decreased by 26.56% and 28.66%.

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RSM based Study of Cutting Temperature During Hard Turning with Multilayer Coated Carbide Insert

Cited by 36 publications

References 11 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

Optimizing production in machining of hardened steels using response surface methodology

The Influence of Tool Rake Surface Geometry on the Hard Turning Process of AISI52100 Hardened Steel

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