PREDICTION OF PROCESS PARAMETERS IN CNC END MILLING OF UNS C34000 MEDIUM LEADED BRASS

Authors

  • Parameshwari N Department of Mechanical Engg, Latha Mathavan Engineering College, Madurai, Tamilnadu- 624401, India
  • Srinivasa Dhaya Prasad S N Department of Mechanical Engg, Latha Mathavan Engineering College, Madurai, Tamilnadu- 624401, India

Keywords:

CNC End Milling, Optimization, Cutting Conditions, DOE, ANOVA, Surface Roughness

Abstract

The study highlights optimization of CNC milling process parameters to provide better surface finish The surface finish has been identified as quality attribute and is assumed to be directly related to productivity.  In order to build up a bridge between quality and productivity, an attempt made to optimize aforesaid quality attribute in small and medium size companies involved with heterogenic product demand. With the more precise demands of modern engineering products, the control of surface texture together with dimensional accuracy has become more important. This paper outlines the Taguchi optimization methodology, which is applied to optimize cutting parameters in end milling operation. The study was conducted in machining operation in UNSC34000 medium leaded brass. The milling parameters evaluated were cutting speed, feed rate and depth of cut. The experiments were conducted by using L-25 orthogonal array as suggested by Taguchi. Signal-to- Noise (S/N) ratio and Analysis of Variance (ANOVA) are employed to analyze the effect of milling parameters on surface roughness. Main effects of process parameters on the quality characteristics have been analyzed. The results show that the optimum parameters of machining by CNC End Milling Machine for given set of parameters.

Downloads

Download data is not yet available.

References

Aggarwal M, Gupta L, Roy B C, Chaudhury S and Walker H F (2002), “Interaction Graphs for A Two-Level Combined Array Experiment Design”, Journal of Industrial Technology, Vol. 18, 67-73.

Benardos P G and Vosniakos G C (2002), “Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments”, Robotics and Computer-Integrated Manufacturing, Vol. 18, 343-354.

Bouzakis K D, Aichouh P and Efstathiou K (2003), “Determination of the chip geometry, cutting force and roughness in free form surfaces finishing milling, with ball end tools”, International Journal of Machine Tool and Manufacture, Vol. 43(5), 499-514.

Bryne M D (1994), “Taguchi’s Approach to Parameters Design”, Journal of Quality Technology, Vol. 26, 39-47.

Dae K B, Tae J K and Hee S K (2005), “Optimization of Feed Rate in a Face Milling Operation Using A Surface Roughness Model”, International Journal of Machine Tools and Manufacture, Vol. 45(3), 293-299.

David S and Victoria (1998), “An Approach to Taguchi Method as a Statistical tool”, The Journal of Technology Studies International Journal of Quality control, Vol. 9, 25-32.

Henri P, Gregoire P and Rene M (2004), “Surface shape prediction in high speed milling”, International Journal of Machine Tools and Manufacture, Vol. 44(15), 1567-1576.

Jiju A and Frenin A (2001), “Taguchi’s Design of Experiment for Engineers”, Journal of Quality Technology, Vol. 34, 59-67.

Yang J L and Chen J C (2001), “A Systematic Approach for Identifying Optimum Surface Roughness Performance in End-Milling Operations”, Journal of Industrial Technology, Vol. 2, 87-99.

Kishawy H A and Elbestawi M A (1999), “Effects of process parameters on material side flow during hard turning”, International Journal of Machine Tools and Manufacture, Vol. 39 (7), 1017-1030.

Savage M D and Chen J C (2004), “Multiple Regression-Based Multilevel In-Process Surface Roughness Recognition System in Milling operation”, International Journal of Machine Tools and Manufacture, Vol. 41(1), 58-67.

Wang M Y and Chang H Y (2004), “Experimental study of surface roughness in slot end milling AL2014-T6”, International Journal of Machine Tools and Manufacture, Vol. 44(1), 51-57.

Edwin R U and Dean B (2002), “Taguchi Approach to Design Optimization for Quality and Cost: an OVER VIEW”, In Proc. Conference of the International Society of Parametric Analysts, 38-57.

Robert B, Jerard Barry and Fussell J K (2000), “Tool path Federate Optimization: A Case Study”, In. Proc NSF Design and Manufacturing Research Conference, Canada.

Ryan T (1998), “Taguchi Approach to Experimental Design”, Journal of Quality Technology, Vol. 26, 224-231.

Steele G, Byers S, Young D and Moore R (1988), “An Analysis of Injection Molding by Taguchi Methods”, In Proc. of ANTEC’88 Conference, 321-328.

Vidal M, Alberti, Ciurana J and Asadesu´ s M (2005), “A Decision Support System for Optimizing the Selection of Parameters When Planning Milling Operations”, International Journal of Machine Tools and Manufacture, Vol. 45(2), 201-210.

Warner J C and Connor J O (1989), “Molding Process is Improved by Using the Taguchi Method”, Modern Plastics, 65-68.

Downloads

Published

2014-03-01

Issue

Section

Articles

How to Cite

[1]
“PREDICTION OF PROCESS PARAMETERS IN CNC END MILLING OF UNS C34000 MEDIUM LEADED BRASS”, JME, vol. 9, no. 1, pp. 036–044, Mar. 2014, Accessed: Dec. 23, 2024. [Online]. Available: https://smenec.org/index.php/1/article/view/293

Similar Articles

51-60 of 304

You may also start an advanced similarity search for this article.