Earticle

초록

영어

In machining operations, the extents of important effect of the process parameters like speed, feed, and depth of cut are different for different responses. This paper investigates the effect of process parameters in turning of AA6061 T6 on conventional lathe. The problem appeared owing to selection of parameters increases the deficiency of turning process. Modeling can facilitate the acquisition of a better understanding of such complex process, save the machining time and make the process economic. Thus, the present work clearly defines the development of an artificial neural network (ANN) model for predicting the material removal rate. This study presents a new method to prediction the material removal rate (MRR) on a lathe turning Process. Firstly, Process parameters namely, Spindle speed, depth of cut and feed rate are designed using the Box behnken (DOE) was employed as the experimental strategy. The result shows that the ANN model can predict the material removal rate effectively. This approach helps in economic lathe machining.

목차

Abstract
 1. Introduction
 2. Experimental Procedure
 3. Design of Experiment
 4. Result and Discussion
  4.1 Development of ANN Model for Prediction of MRR
 5. Conclusion
 References

키워드

저자

• Vinay Kumar Chaurasia [ Department of Mechanical Engineering Madhav Institute of Technology & Science, Gwalior- 474005, India (M.P) ] Corresponding Author
• Dinesh Kumar Kasdekar [ Department of Mechanical Engineering Madhav Institute of Technology & Science, Gwalior- 474005, India (M.P) ]
• Vaibhav Shivhare [ Department of Mechanical Engineering Madhav Institute of Technology & Science, Gwalior- 474005, India (M.P) ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605