Production Engineering

, 3:417 | Cite as

Parametric optimization of ultrasonic machining of co-based super alloy using the Taguchi multi-objective approach

Production Process

Abstract

Stellite 6 is the most generally useful cobalt alloy, having excellent resistance to many forms of mechanical and chemical degradation over a wide temperature range. Therefore, in particular, their use in aerospace projects world wide has provided confidence leading to acceptance as prime material for aerospace vehicles in recent years. This paper outlines the effectiveness of the ultrasonic machining of stellite 6 in terms of tool wear rate of the tool used and the material removal rate of work piece produced. The optimum combination of various input factors as type of abrasive slurry, their size and concentration, nature of tool material and power rating of the machine for the ductile chip formation in the machining of stellite 6 has been determined by applying the Taguchi multi-objective optimization technique and F-test. The analysis of results has been done using the statistica 7.0 software and results obtained are validated by conducting the confirmation experiments. The study shows the considerable improvement in multiple S/N ratio as compared to initial cutting conditions.

Keywords

Multi-objective Ultrasonic Wear Tool Optimization MRR 

References

  1. 1.
    Sanchez AJ (2006) Study on gap variation in multi-stage planetary EDM. Int J Mach Tools Manuf 46:1598–1603CrossRefGoogle Scholar
  2. 2.
    Wang H, Wu CL (2000) The mechanism of material removal in ultrasonic drilling of engg ceramics. Proc Inst Mech Eng 214:805–810CrossRefGoogle Scholar
  3. 3.
    Hu P, Zang JM, Pei ZJ, Treadwell C (2002) Modeling of material removal rate in rotary ultrasonic machining: designed experiment. J Materi Process Technol 129:339–344CrossRefGoogle Scholar
  4. 4.
    Roy RK (1990) A premier on the Taguchi method. Van Nostrand Reinhold, New YorkGoogle Scholar
  5. 5.
    Jiang BC, Black JT, Hool JN, Wu CM (1989) Determining robot process capability using Taguchi methods. Robot Comp Integr Manuf 6:55–66CrossRefGoogle Scholar
  6. 6.
    Jiang BC, Black JT, Chen DW, Hool JN (1991) Taguchi based methodology for determining/optimizing robot process capability. IIE Transac 23:169–184CrossRefGoogle Scholar
  7. 7.
    Rim WT, Jang HK, Kim KJ (1991) Dynamic performance improvement of an electrical discharge machine using an experimental model analysis. Int J Mach Tools Manuf 31:305–314CrossRefGoogle Scholar
  8. 8.
    Tam SC, Loh NH, Miyazawa S (1989) Optimization of the ECM-abrasive polishing of mild steel using response surface methodology. J Mech Work Technol 19:109–117CrossRefGoogle Scholar
  9. 9.
    Chanin MN, Kuei CH, Lin C (1990) Using Taguchi design, regression analysis and simulation to study maintenance float systems. Int J Prod Res 28:1939–1953CrossRefGoogle Scholar
  10. 10.
    Kuei CH, Madu CN (1994) Sequential experimentation and multilevel Taguchi designs in simulation meta modeling of a maintenance float problem. Microelectron Reliab 34:831–843CrossRefGoogle Scholar
  11. 11.
    Benton WC (1991) Statistical process control and the Taguchi method: a comparative evaluation. Int J Prod Res 29:1761–1770CrossRefGoogle Scholar
  12. 12.
    Chao PY, Hwang YD (1995) The investigation of metal cutting experiments by using of Taguchi-based methodology. Proc NSCD Part A Phys Sci Eng 19:295–308Google Scholar
  13. 13.
    Forouraghi B (1999) On utility of inductive learning in multi-objective robust design, artificial intelligence for engineering design. Anal Manuf 13:27–36Google Scholar
  14. 14.
    Hunga YH, Haung ML, Chang CH (2006) Optimizing the controller IC for micro HDD process based on Taguchi method. Microelectron Reliab 46:1183–1188CrossRefGoogle Scholar
  15. 15.
    Mohammadi T, Moheb A (2004) Separation of copper ions by electro dialysis using Taguchi experimental design. Desalination 169:21–31CrossRefGoogle Scholar
  16. 16.
    Mertol A (2000) Application of the Taguchi method to chip scale package (CSP) design. IEEE Trans Adv Package 23:266–276CrossRefGoogle Scholar
  17. 17.
    Lin TR (2002) Optimization technique for face milling stainless steel with multiple performance characteristic. Int J Adv Manuf Technol 19:330–335CrossRefGoogle Scholar
  18. 18.
    Chang CW, Kuo CP (2007) Evaluation of surface roughness in laser assisted machining of aluminum oxide ceramics with Taguchi method. Int J Mach Tools Manu 47:141–147MATHCrossRefGoogle Scholar

Copyright information

© German Academic Society for Production Engineering (WGP) 2009

Authors and Affiliations

  1. 1.Mechanical Engineering DepartmentThapar UniversityPatialaIndia
  2. 2.Mechanical Engineering Department, UCOEPunjabi UniversityPatialaIndia

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