International Journal of Advance Research and Innovation
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- Article · September 2014 CITATIONS 5 READS 2,879 4 authors
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See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/272575688 Parametric Analysis of Surface Roughness Studies in Turning Using Artificial Neural Network Article · September 2014 CITATIONS 5 READS 2,879 4 authors , including: Some of the authors of this publication are also working on these related projects: Dates Extended for Full manuscript Submission at ICAPIE 2016, Delhi, India View project Dates Extended for Full manuscript Submission at ICAPIE 2016, Delhi, India View project Ranganath M Singari Delhi Technological University, Formerly Delhi college of Engineering, Delhi, India 177 PUBLICATIONS 958 CITATIONS SEE PROFILE Vipin vp Delhi Technological University 64 PUBLICATIONS 650 CITATIONS SEE PROFILE Sonu Yadav Indian Institute of Information Technology Allahabad 1 PUBLICATION 5 CITATIONS SEE PROFILE All content following this page was uploaded by Ranganath M Singari on 22 February 2015. The user has requested enhancement of the downloaded file. Volume 2, Issue 3 (2014) 676-683 ISSN 2347 - 3258 International Journal of Advance Research and Innovation 676 IJARI Parametric Analysis of Surface Roughness Studies in Turning Using Artificial Neural Network Ranganath M. S. *, a , Vipin a , Sudhanshu Maurya b , Sonu Yadav b a Department of Production and Industrial Engineering, Delhi Technological University, New Delhi, India b Department of Mechanical Engineering, Delhi Technological University, New Delhi, India Abstract Neural Networks are information processing systems and can be used in several areas of engineering applications and eliminate limitations of the classical approaches by extracting the desired information using the input data. The advantage of the usage of neural networks for prediction is that they are able to learn from examples only and that after their learning is finished, they are able to catch hidden and strongly nonlinear dependencies, even when there is significant noise in the training set. One of the most specified customer requirements in a machining process is surface roughness. For efficient use of machine tools, optimum cutting parameters are required. Therefore it is necessary to find a suitable optimization method which can find optimum values of cutting parameters for minimizing surface roughness. The turning process parameter optimization is highly constrained and nonlinear. Many researchers have used an artificial neural network (ANN) model for the data obtained through experiments to predict the surface roughness. The results obtained, conclude that ANN is reliable and accurate for solving the cutting parameter optimization. The paper work presents on all studies where ANN has been used to analyse surface roughness in turning process. Yüklə 0,71 Mb. Dostları ilə paylaş:
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