TURN-ASSISTED DEEP COLD ROLLING - A COST EFFECTIVE MECHANICAL SURFACE TREATMENT TECHNIQUE FOR SURFACE HARDNESS ENHANCEMENT

Authors

  • Prabhu P R Department of Mechanical & Manufacturing Engineering, Manipal Institute of Technology, Manipal, Karnataka 576104 India
  • Satyabodh Kulkarni Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, Karnataka 575025, India
  • Sathya Shankara Sharma Department of Mechanical & Manufacturing Engineering, Manipal Institute of Technology, Manipal, Karnataka 576104 India

Keywords:

Turning, Deep Cold Rolling, Surface Hardness, Response Surface Methodology , Mechanical Surface Treatment

Abstract

In the present study, an optimization strategy based on desirability function approach together with response surface methodology has been used to optimize turn-aided deep cold rolling process of AISI 4140 steel. A regression model is developed to predict surface hardness. In the development of predictive model, rolling force, ball diameter, initial roughness of the workpiece, and number of tool passes are considered as model variables. The rolling force and ball diameter are found to be the significant factors on surface hardness. The predicted surface hardness values and the subsequent verification experiments under the optimal conditions confirmed the validity of the predicted model. The absolute average error between the experimental and predicted values at the optimal combination of parameter settings for surface hardness is calculated as 0.97%. Using the optimal processing parameters, the hardness is improved from 225 to 306 HV, which resulted in an increase in the near surface hardness by about 36%. The depth of compression is found to be more than 300 µm obtained from the micro hardness measurements.

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References

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Published

2014-03-01

How to Cite

[1]
Prabhu P R, Satyabodh Kulkarni, and Sathya Shankara Sharma, “TURN-ASSISTED DEEP COLD ROLLING - A COST EFFECTIVE MECHANICAL SURFACE TREATMENT TECHNIQUE FOR SURFACE HARDNESS ENHANCEMENT”, JME, vol. 9, no. 1, pp. 022–029, Mar. 2014.

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