Experimental Investigation of Tool Offset Variation on Cutting Tool Vibration

Authors

  • Ashish George J Department of Mechanical Engineering, Ramaiah Institute of Technology
  • Mr. Paramesh Department of Mechanical Engineering, Ramaiah Institute of Technology, MSR Nagar, Bengaluru, Karnataka,
  • Mr. Manoj Department of Mechanical Engineering, Ramaiah Institute of Technology, MSR Nagar, Bengaluru, Karnataka,
  • K. Lokesha Department of Mechanical Engineering, Ramaiah Institute of Technology, MSR Nagar, Bengaluru, Karnataka,

Keywords:

ANOVA, Machining, Optimisation, Taguchi, Vibration Monitoring

Abstract

Machining has been one of the major manufacturing processes used in the industries over the years to produce high-quality products, where many parameters influence the quality of the products which include dimensional accuracy, cutting tool vibration, surface roughness, etc. In present times, advanced manufacturing industries aim at developing components by optimizing the process parameters enabling them to effectively utilise the available resources, thus saving time and cost involved. In this context, an effort has been made to investigate the significance of tool offset for turning operation of a mild steel material where experiments are performed on a lathe by varying process parameters such as spindle speed, feed rate and depth of cut. Experiments are designed using Taguchi L9 orthogonal array technique, and for every trial of experiments, cutting tool vibrations are captured with the help of tri-axial accelerometer. The influence of process parameters on vibration is analysed using analysis of variance technique which depicts that there is a significant effect of offsetting the tool on cutting tool vibration.

References

Claesson I, Hakansson L. Active control of machine-tool vibration in a lathe. Fifth International Congress on Sound and Vibration. Adelaide, Australia. 1997, December 15–18.

Singh JK, Bhardwaj SK. Optimisation of the cutting parameters by vibration analysis of cutting tool. Int J Latest Trends Eng Technol. 2015; 5(1): 270–275p.

Raut LB, Shaikh MA. Prediction of vibrations, cutting force of single point cutting tool by using artificial neural network in turning. Int J Mec Eng Technol. 2014; 5(7): 125–127p.

Ahewar A, Unune D, Pathri B, Kishan J. Statistical and regression analysis of vibration of carbon steel cutting tool for turning of EN24 steel using design of experiments. Int J Recent Adv Mech Eng. 2014; 3(3): 137–151p.

Subramanian M, Sakthivel M, Sooryaprakash K, Sudhakaran K. Optimisation of end mill tool geometry parameters for A17075-T6 machining operations based on vibration amplitude by response surface methodology. Measurement. 2014; 46: 401–407p.

Kolhe BP, Rahane SP, Galhe DS. Prediction and control of lathe machine tool vibration – a review. Int J Adv Res Innov Ideas Educ. 2015; 1(3): 153–156p.

Khandait SS, Vanalkar AV. Condition monitoring of single point cutting for lathe machine using FFT analyser – a review. Int J Sci Res. 2015; 6(6): 664–662p.

Malgave PS, Kulkarani SS, Shrotri AP, Dandekar AP. Tool condition monitoring in machining using vibration signature analysis: a review. Int J Adv Eng Sci Technol. ISSN: 2319-1112/V5N1.

Patil VD, Sali SP. Process parameter optimization for computer numerical control turning on En36 alloy steel. International Conference on Nascent Technologies in the Engineering Field. Navi Mumbai, India. 2017, January 27–28.

Chaudhari NB, Yerrawar RN, Gawande SH. Experimental investigation of machine tool vibration in SS304 turning. Int J Adv Ind Eng. 2015; 3(3): 120–127p.

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Published

2019-01-28

Issue

Vol. 4 No. 2 (2018)

Section

Articles