Influence of Cutting Parameters in Face Milling Semi-Solid AA 2024 Using a Carbide Tool Affecting the Surface Roughness and Tool Wear

Surasit RAWANGWONG; Jaknarin CHATTHONG; Worapong BOONCHOUYTAN; Chatree HOMKHIEW; Watthanaphon CHEEWAWUTTIPONG; Romadorn BURAPA

Authors

Surasit RAWANGWONG Materials Processing Technology Research Unit, Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Srivijaya, Muang, Songkhla 90000
Jaknarin CHATTHONG Materials Processing Technology Research Unit, Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Srivijaya, Muang, Songkhla 90000
Worapong BOONCHOUYTAN Materials Processing Technology Research Unit, Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Srivijaya, Muang, Songkhla 90000
Chatree HOMKHIEW Materials Processing Technology Research Unit, Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Srivijaya, Muang, Songkhla 90000
Watthanaphon CHEEWAWUTTIPONG Materials Processing Technology Research Unit, Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Srivijaya, Muang, Songkhla 90000
Romadorn BURAPA Materials Processing Technology Research Unit, Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Srivijaya, Muang, Songkhla 90000

Keywords:

CNC milling machine, semi-solid AA 2024, surface roughness, tool wear

Abstract

The aim of this research was to investigate the effects of the main factors on the surface roughness in aluminum semi-solid 2024 face milling. This study was conducted using a computer numerical controlled milling machine having 63 mm diameter fine type carbide tool with a twin cutting edge. The variable factors investigated were speed, feed rate and depth of cut being not over 1 mm. For this experiment, the factorial designs were applied. The results revealed that the factors that significantly affect the surface roughness were the speed and feed rate; while the depth of cut did not affect the result. An increase in the speed and a decrease in the feed rate tended to reduce the surface roughness. Besides, the results obtained from this research could be employed to generate the linear equation of R_a = 0.205 - 0.000022 Speed + 0.000031 Feed rate. Lastly, the flank and crater wears were observed at a tool life of 1,450 min.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Author Biography

Surasit RAWANGWONG, Materials Processing Technology Research Unit, Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Srivijaya, Muang, Songkhla 90000

Industrial Engineering

References

M Alauddin, MA Baradie, MSJ Hashmi. Computer-aided analysis of a surface-roughness model for end milling. J. Mater. Proc. Tech. 1995; 55, 123-7.

MS Lou, JC Chen and CM Li. Surface roughness prediction technique for CNC end milling. J. Ind. Tech. 1998; 15, 1-6.

MS Lou and JC Chen. In-process surface roughness recognition system in end-milling operations. Int. J. Adv. Manuf. Tech. 1999; 15, 200-9.

F Dweiri, M Al-Jarrah and H Al-Wedyan. Fuzzy surface roughness modeling of CNC down milling of Alumic-79. J. Mater. Proc. Tech. 2003; 133, 266-75.

JA Ghani, IA Choudhury and HH Hassan. Application of Taguchi method in the optimization of end milling parameters. J. Mater. Proc. Tech. 2004; 145, 84-92.

M Brezocnik, M Kovacic and M Ficko. Prediction of surface roughness with genetic programming. J. Mater. Proc. Tech. 2004; 157-158, 28-36.

MY Wang and HY Chang. Experimental study of surface roughness in slot end milling AL2014-T6. Int. J. Mach. Tools Manuf. 2004; 44, 51-7.

H Oktem, T Erzurumlu and H Kurtaran. Application of response surface methodology in the optimization of cutting conditions for surface roughness. J. Mater. Proc. Tech. 2005; 170, 11-6.

H Oktem, T Erzurumlu and F Erzincanli. Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm. Mater. Des. 2006; 27, 735-44.

W Wang, SH Kweon and SH Yang. A study on roughness of the micro-end-milled surface produced by a miniatured machine tool. J. Mater. Proc. Tech. 2005; 162-163, 702-8.

NSK Reddy and PV Rao. Selection of optimum tool geometry and cutting conditions using surface roughness prediction model for end milling. Int. J. Adv. Manuf. Tech. 2005; 26, 1202-10.

B Ozcelik and M Bayramoglu. The statistical modeling of surface roughness in high speed flat end milling. Int. J. Mach. Tools Manuf. 2006; 46, 1395-402.

SH Ryu, DK Choi and CN Chu. Roughness and texture generation on end milled surfaces. Int. J. Mach. Tools Manuf. 2006; 46, 404-12.

E Bagci and S Aykut. A study of Taguchi optimization method for identifying optimum surface roughness in CNC face milling of cobalt-based alloy (satellite 6). Int. J. Adv. Manuf. Tech. 2006; 29, 940-47.

CK Chang and HS Lu. Design optimization of cutting parameters for side milling operations with multiple performance characteristics. Int. J. Adv. Manuf. Tech. 2007; 32, 18-26.

R Burapa, S Janudom, T Chucheep, R Canyook and J Wannasin. Effects of primary phase morphology on mechanical properties of Al-Si-Mg-Fe alloy in semi-solid slurry casting process. Trans. Nonfer. Met. Soc. China 2010; 20, 857-61.

K Ploypanichjaroen. Engineering Statistics 2: Minitab Processed. 4th ed. Technology Promotion Association (Thai-Japanese), Bangkok, 2003.

DC Montgomery. Design and Analysis of Experiments. 6th ed. John Wiley & Son, New York, 2005.

P Sutus Na Ayuthaya and P Luengpaiboon. Design and Analysis of Experiment. Top Publishing, Bangkok, 2008.

AG Jaharah, AMNAM Rodzi, RM Nizam and CHC Hassan. Machinability of FCD 500 ductile cast iron using coated carbide tool in dry machining condition. Int. J. Mech. Mater. Eng. 2009; 4, 279-84.

YJ Lin and SK Khrais. Wear mechanisms and tool performance of TiAlN PVD coated inserts during machining of AISI 4140 steel. Wear 2007; 262, 64-9.

KVBSK Kumar and SK Choudhury. Investigation of tool wear and cutting force in cryogenic machining using design of experiments. J. Mater. Proc. Tech. 2008; 203, 95-101.