Prediction of Surface Roughness in High Speed Machining of Inconel 718

Mokhtar Suhaily; A.K.M. Nurul Amin; Anayet Ullah Patwari

doi:10.4028/www.scientific.net/AMR.264-265.1193

Paper Titles

Surface Roughness Prediction in High Speed Flat End Milling of Ti-6Al- 4V and Optimization by Desirability Function of RSM
p.1166

Chip Serration Frequency - The Primary Cause of Chatter during End Milling Operation
p.1174

Influence of Electrode Cooling on Recast Layers and Micro Crack in EDM of Titanium
p.1180

Investigation of the Machinability of Inconel 718 in Room Temperature and Preheated Conditions
p.1187

Prediction of Surface Roughness in High Speed Machining of Inconel 718
p.1193

Influence of Electrode Cooling on Material Migration among the Electrodes during EDM of Titanium Alloy
p.1199

Influence of Electrode Profiles on Electrical Discharge Machining of Titanium
p.1205

The Effect of Tool Edge Geometry on Tool Performance and Surface Integrity in Turning Ti-6Al-4V Alloys
p.1211

A Study of Jet Characteristics of Plasma Arc under Transverse Alternating Magnetic Field
p.1222

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 264-265Prediction of Surface Roughness in High Speed...

Prediction of Surface Roughness in High Speed Machining of Inconel 718

Abstract:

Surface finish and dimensional accuracy is one of the most important requirements in machining process. Inconel 718 has been widely used in the aerospace industries. High speed machining (HSM) is capable of producing parts that require little or no grinding/lapping operations within the required machining tolerances. In this study small diameter tools are used to achieve high rpm to facilitate the application of low values of feed and depths of cut to investigate better surface finish in high speed machining of Inconel 718. This paper describes mathematically the effect of cutting parameters on Surface roughness in high speed end milling of Inconel 718. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. Machining were performed using CNC Vertical Machining Center (VMC) with a HES510 high speed machining attachment in which using a 4mm solid carbide fluted flat end mill tool. Wyko NT1100 optical profiler was used to measure the definite machined surface for obtaining the surface roughness data. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to predict the surface roughness value with in the specified cutting conditions limit.

You might also be interested in these eBooks

Advances in Materials and Processing Technologies II

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 264-265)

Pages:

1193-1198

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.264-265.1193

Citation:

Cite this paper

Online since:

June 2011

Authors:

Mokhtar Suhaily, A.K.M. Nurul Amin, Anayet Ullah Patwari

Keywords:

High Speed Machining (HSM), Inconel 718, RSM, Surface Roughness (SR)

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] M. Balazinski, V. Songmene, Improvement of tool life through variable feed milling of inconel 600, Ann. CIRPP 44 (1) pp.55-58, (1995).

DOI: 10.1016/s0007-8506(07)62274-4

Google Scholar

[2] N. Natural, Y. Yamaha, High speed machining of inconel 718 with ceramic tools, Ann. CIRP 42 (1) pp.103-106, (1993).

DOI: 10.1016/s0007-8506(07)62402-0

Google Scholar

[3] M. Alauddin, M. A. El-Baradie, M.S. J Hashmi, End milling machinability of inconel 718, Int. Journal Eng. Manuf., Vol 210, pp.11-23, (1996).

DOI: 10.1243/pime_proc_1996_210_082_02

Google Scholar

[4] N. Richard and Aspinwal, Use of ceramic tools for machining nickel-based alloys, Int. J. Mach. Tool Manufact., Vol. 29, pp.575-588, (1989).

Google Scholar

[5] M.C. Shaw, Metal Cutting Principles, 2nd edition, London, Oxford University Press, (1986).

Google Scholar

[6] Babur Ozcelik, Hasan Oktem, Hasan Kurtaran, Optimum surface roughness in end milling inconel 718 by coupling neural network model and genetic algorithm, Int. J. Adv. Manuf. Technol. Vol 27, pp.234-241, (2005).

DOI: 10.1007/s00170-004-2175-7

Google Scholar

[7] S. Y. Lin, J. C Lin, C.C. Lin, W.Y. Jywe, B. J. Lin, Life prediction system using a tool's geometric shape for high-speed milling, Int. J. Adv. Manufact. Technol, Vol. 30, pp.622-630, (2006).

DOI: 10.1007/s00170-005-0116-8

Google Scholar

[8] R. M. Arunachalam, M.A. Mannan, A. C Spowage, Residual stress and surface roughness when facing age hardened Inconel 718 with CBN and ceramic cutting tools, Int. J. Mach. Tool & Manufact., Vol. 44, pp.879-887, (2004).

DOI: 10.1016/j.ijmachtools.2004.02.016

Google Scholar

[9] K. Kadirgama, M.M. Noor, N.M. Zuki N. M, M. M Rahman, M.R.M. Rejab, R. Daud, K. a. Abou-El-Hossein, Optimization of surface roughness in end milling on mould aluminium alloys (AA6061-T6) using response surface method and radian basis function network, Jordan. Journal of Mechanical Industrial Engineering, Vol. 2, Number 4, pp.209-214, December (2008).

Google Scholar

[10] M. Alauddin, M.A. EL Baradie, M.S. J Hashmi, Prediction of tool life in end milling by response surface methodolog, Vol. 71, 1997, 445-465.

DOI: 10.1016/s0924-0136(97)00111-8

Google Scholar

[11] Dagnal MA 91986) Exploring surface texture. Rank Taylor Hobson, Leicester.

Google Scholar

[12] M. Ishtiyaq Hossain, AKM. N. Amin, A.U. Patwari, " Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy, Proceedings of the International Conference on computer and Communication Engineering 2008, pp.1321-1324 May 13-15 (2008).

DOI: 10.1109/iccce.2008.4580819

Google Scholar

[13] Mitsubishi Materials USA, Carbide and Tools Division catalogue (2007).

Google Scholar