Open Access   Article

Modeling the Process Parameters of Roller Burnishing using RSM and Prediction of Micro Hardness using Artificial Neural Network

Vijay Kurkute1 , Sandip Chavan2

1 Sinhgad College of Engineering Pune, Savitribai Phule Pune University, Pune (India).
2 Dept. Mechanical Engineering MAEER’s Maharashtra Institute of Technology, Pune (India).

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-4 , Page no. 43-50, Apr-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i4.4350

Online published on Apr 30, 2018

Copyright © Vijay Kurkute, Sandip Chavan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: Vijay Kurkute, Sandip Chavan, “Modeling the Process Parameters of Roller Burnishing using RSM and Prediction of Micro Hardness using Artificial Neural Network”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.43-50, 2018.

MLA Style Citation: Vijay Kurkute, Sandip Chavan "Modeling the Process Parameters of Roller Burnishing using RSM and Prediction of Micro Hardness using Artificial Neural Network." International Journal of Computer Sciences and Engineering 6.4 (2018): 43-50.

APA Style Citation: Vijay Kurkute, Sandip Chavan, (2018). Modeling the Process Parameters of Roller Burnishing using RSM and Prediction of Micro Hardness using Artificial Neural Network. International Journal of Computer Sciences and Engineering, 6(4), 43-50.

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Abstract

Neural network computational techniques are a new alternative to conventional numerical modeling. This paper presents modeling using response surface methodology (RSM). Box and Wilson Central Composite Design (CCD) is used for preparing experiment matrix. The independent parameters in the experiment are speed, feed, force and number of tool passes. These variables are controlled during the burnishing process. The response parameter is micro hardness. Experimental samples are prepared using Single Roller Burnishing Tools (Carbide). Vickers micro hardness tester is used to measure micro hardness. A quadratic mathematical model is developed using RSM. An Artificial neural network (ANN) model is developed using three-layer feed-forward back-propagation. The neural network model is trained using measured values of micro hardness. The different algorithms are used to train the model. Best performance is achieved with correlation coefficient 0.9. This study concludes that an artificial neural network is the best alternative to fit the nonlinear data.

Key-Words / Index Term

Burnishing , RSM, Micro Hardness, ANN

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