Tool wear mechanisms and multi-response optimization of tool life and volume of material removed in turning of N-155 iron–nickel-base superalloy using RSM

Davoodi, Behnam; Eskandari, Behzad

doi:10.1016/j.measurement.2015.03.006

Cited by 98 publications

(37 citation statements)

References 29 publications

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“…The researchers attributed this to shortened duration of contact between cutting tool and workpiece. In contrast to this result, Davoodi and Eskandri [36] found a significant relationship between the two variables. In the study, effects of various cutting speed (i.e.…”

Section: Tool Wearcontrasting

confidence: 77%

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Effects of cutting parameters on machinability characteristics of Ni-based superalloys: a review

Kaya

Akyüz

2017

Open Engineering

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Nickel based superalloys offer high strength, corrosion resistance, thermal stability and superb thermal fatigue properties. However, they have been one of the most difficult materials to machine due to these properties. Although we are witnessing improved machining strategies with the developing machining, tooling and inspection technologies, machining of nickel based superalloys is still a challenging task due to in-process strains and post process part quality demands. Selecting optimum machining parameters for quality, productivity and profitability is of paramount importance. Many studies have been conducted on various aspects of machinability of nickel based superalloys including defining the optimum cutting parameters, to develop a better understanding of machining them. The recent studies suggest new findings, and discuss previously reported results, related to the concerns of superalloy machining. This review presents the influences of the most significant cutting parameters on various machinability characteristics with respect to the recent studies as well as the previous ones. The reviewed machinability characteristics may be listed as: tool wear, cutting forces and surface integrity.

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Section: Tool Wearcontrasting

confidence: 77%

“…Cutting speed was reported as one of the most significant parameters of tool wear in machining of the superalloys [31][32][33][34][35][36]. D'Addona et al [37] investigated tool wear and surface roughness in high speed machining of Inconel 718.…”

Section: Tool Wearmentioning

confidence: 99%

Effects of cutting parameters on machinability characteristics of Ni-based superalloys: a review

Kaya

Akyüz

2017

Open Engineering

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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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“…This is a technique used to develop statistical and mathematical models between experimental input variables and output variables like BSFC, BTE, ME and VE. These models are used to predict responses and to optimize process variables [18]. In RSM, the quantitative relationship between input and output variables is presented in (3.1) as follows [19]:…”

Section: Response Surfacementioning

confidence: 99%

Experimental Investigation on Performance of Four Stroke C.I. Engine with Blends of Diesel and MOME using Response Surface Methodology

Kondaiah¹,

Prasad²

2016

JAMES

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There is rapid depletion of diesel resources due to increased demand of diesel in the fields of agriculture, power production and transportation. Use of biodiesel can help reduce the dependability on diesel. In this study, biodiesel is prepared through the process of transesterification of mahua oil. Experiments are conducted on single cylinder internal combustion (IC) engine at different loads, compression ratios and blends of biodiesel. Experimental results viz., brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), volumetric efficiency (VE) and mechanical efficiency (ME) are calculated. Experimental results are analyzed using response surface methodology to identify significant parameters for each response and mathematical models are developed. A multi response optimization technique is used to optimize the parameters for minimum BSFC and maximum BTE, VE and ME. Optimum process parameters are found as 3.0142 KW of load, 19.79 of compression ratio and 20% of blends.

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Tool wear mechanisms and multi-response optimization of tool life and volume of material removed in turning of N-155 iron–nickel-base superalloy using RSM

Cited by 98 publications

References 29 publications

Effects of cutting parameters on machinability characteristics of Ni-based superalloys: a review

Effects of cutting parameters on machinability characteristics of Ni-based superalloys: a review

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

Experimental Investigation on Performance of Four Stroke C.I. Engine with Blends of Diesel and MOME using Response Surface Methodology

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