Prediction of Surface Roughness Using Artificial Neural Network in Single Point Diamond Turning

Anandhan, M and Ramagopal, S.V. and Hariharan, P (2013) Prediction of Surface Roughness Using Artificial Neural Network in Single Point Diamond Turning. International Journal of Scientific Research, 2 (6). pp. 220-223. ISSN 2277 - 8179

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In manufacturing industries, manufacturers focused on the quality and productivity of the product. To increase the productivity of the product, computer numerically machine tools have been implemented during the past decades. Surface roughness is one of the most important parameters to determine the quality of product. The mechanism behind the formation of surface roughness is very dynamic, complicated, and process dependent. Several factors will influence the final surface roughness in a Diamond turning operations such as controllable factors (spindle speed, feed rate and depth of cut) and uncontrollable factors (tool geometry and material properties of both tool and work piece). Some of the machine operator using trial and error method to set-up machine cutting conditions. This method is not much effective and efficient and the achievement of a desirable value is a repetitive and empirical process that can be very time consuming. In order to solve the problem, a surface prediction technique based on artificial neural network prediction models is developed to predict the machining response for different input machining parameters. Thus, manufacturers can improve the quality and productivity of the product with minimum cost and time.

Item Type: Article
Subjects: CSIO > Optics
Depositing User: Ms. Jyotsana
Date Deposited: 09 Aug 2018 12:02
Last Modified: 09 Aug 2018 12:02

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