Investigating The Effect of Machining Parameter on Milling Product Surface Quality Using Doe Approach

Authors

  • Muhammad Yusuf
  • Teuku Hafli

Keywords:

DoE, cutting_parameters, surface_roughness

Abstract

Machining process is one of the complex processes which have numerous factors contributing towards the quality of the finished product. Milling process is one among the machining process in which quality of the finished product depends on the tool geometry, accuracy of machine tool and cutting condition. The research was done to investigate the effect of machining parameters on product surface quality in milling process. The experiments conducted on a conventional milling machine use of cutting fluid. Material AISI 1050 and HSS end mill cutting tool used for the experiments. Three cutting parameters such as spindle speed, feed rate and depth of cut at two-level was planned using design of experiment (DoE) methodology. The result show that the better surface quality of milling process is obtained for the minimum value of surface roughness (Ra = 1.481 μm). The Analysis of Variance inferred that the spindle speed cutting parameter has significant effect on the surface roughness (Ra).

References

Bass, I. (2007). Six sigma statistics with excel and Minitab. McGraw-Hill Companies, Inc. USA. Boothroyd, G. and Knight, W.A. (2006). Fundamentals of Machining and Machine Tools. Taylor & Francis Group. Denkena, B. and Hasselberg, E. (2015).

Influence of the cutting tool compliance on the workpiece surface shape in face milling of workpiece compounds, In Procedia CIRP Vol. 31, pp. 7 – 12. Groover, M.P. (2010).

Fundamentals of Modern Manufacturing: Materials, Processes, and Systems. John Wiley & Son. Myers, R.H. and Montgomery, D.C. (2002). Response Surface Methodology. John Wiley & Sons. Rashid, A. and Lani, A. (2010).

Surface Roughness Prediction for CNC Milling Process using Artificial Neural Network, In Proceedings of the World Congress on Engineering, Vol III WCE 2010, London, U.K. Stephenson, D.A. and Agapiou, J.S. (2006).

Metal Cutting Theory and Practice. Taylor & Francis Group 2006. Tseng, T., Konada, U. and Kwon, Y. (2016).

A novel approach to predict surface roughness in machining operations using fuzzy set theory Journal of Computational Design and Engineering Vol.3, pp. 1–13. Yusuf, M., Ariffin, M.K.A., Ismail, N. and Sulaiman, S. (2013).

Chip formation and surface roughness in dry machining of aluminium alloys, Advanced Science Letters, Vol. 19, pp. 2343-2346.

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Published

2018-12-31