Study on surface roughness measurement for turning of Al 7075/10/SiCp and Al 7075 hybrid composites by using response surface methodology (RSM) and artificial neural networking (ANN)

Kumar, Ravinder; Chauhan, S.R.

doi:10.1016/j.measurement.2015.01.003

Cited by 179 publications

(81 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Truly, after predicting tensile strength of friction stir welded AA7039 aluminum alloy joints, Lakshminarayanan and Balasubramanian [14], concluded that RSM has a main advantage compared with ANN, this advantage consist of its ability to quantify the factor contributions from the coefficients in the regression model, identifying the insignificant main factors and interaction factors or insignificant terms in the model. Moreover, in their comparison between ANN and RSM approaches for modeling surface roughness when turning of Al7075/10/SiCp and Al 7075 hybrid composites, Kumar and Chauhan [15], concluded that the ANN prediction model produced a greater parentage error than the RSM prediction model with (R 2 ) values of 0.99571 and 0.9972 respectively.…”

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.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…The optimal value obtained were 4 m/min for cutting speed, 0.1 mm/rev for feed rate and point angle 135°.Also, for achieving lower surface roughness, lower feed rate and cutting speed is preferred. The surface measurement for the Turing of Aluminium metal matrix composite with the combination of Al7075/10/SiCp was studied by Ravinder Kumar and Santram Chauhan [18] using two techniques. The cutting speed, feed and approach angle were varied within a range and the experiments were conducted.…”

Section: Fig 6 Microstructure Of Machined MMC [15]mentioning

confidence: 99%

A Comprehensive Study on the Process Parameters for Machining Aluminium 7075

P¹

2017

IJRASET

View full text Add to dashboard Cite

This paper attempts to make a detail study on the machining parameters of Aluminium 7075 either in its pure form or in the form of composites. This grade of aluminium is known for its application in aerospace industry and hence reviewing about its machining parameters will lead to more development in the field of production. Surface roughness is a phenomenon which affects the product gradually and hence its influence on Al 7075 is important to deal with. The process parameters of speed, feed and depth of cut are the common interest in this study and their role in the various design of experiments techniques. From the discussions, it is revealed that feed rate is the most influencing parameter amongst all and hence this particular parameter could be very crucial in the scientific investigation pertaining to the use of Aluminium 7075.

show abstract

“…Al MMC reinforced with varying percentage by weight of SiC is considered as potential candidate material due to its light weight and improved thermo mechanical properties for brake application. This process involves a liquid state fabrication technique which requires the incorporation of reinforcing phase (discontinuous form) into a molten matrix metal (continuous form) to obtain a uniform distribution through stirring [27][28][29][30].…”

Section: Selection Of Materialmentioning

confidence: 99%

Design analysys of disk brake using newly developed stir casted master alloy disk for improved cooling

2017

IJLTET

View full text Add to dashboard Cite

Study on surface roughness measurement for turning of Al 7075/10/SiCp and Al 7075 hybrid composites by using response surface methodology (RSM) and artificial neural networking (ANN)

Cited by 179 publications

References 16 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 Comprehensive Study on the Process Parameters for Machining Aluminium 7075

Design analysys of disk brake using newly developed stir casted master alloy disk for improved cooling

Contact Info

Product

Resources

About