ANN Based Prediction Model as a Condition Monitoring Tool for Machine Tools
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2014, Vol 4, Issue 11
Abstract
In today’s world of manufacturing, a machine tool has an important role to produce best quality and quantity in phase with the demand. Machine tool in good working condition enhances the productivity and provides an opportunity for the overall development of the industry. Several parameters such as vibration of a machine tool structure, temperature at cutting zones, machined surface roughness, noise levels in the moving parts etc. provide the information of its working condition which is related to its productivity. Surface roughness of the machined part is one of the parameters to indicate machine tool condition. In the recent trends, soft computing tools have emerged as an aid for the condition monitoring of machine tools. In the present work, experiments have been conducted based on Taguchi technique for turning operation with different process input parameters using carbide cutting tool insert and the surface roughness of the turned parts were measured as output characteristic. Relation between input and output was established and a prediction model for surface roughness was built by using artificial neural network (ANN) backpropagation learning algorithm. The predicted values of surface roughness from the prediction model in comparison with the experimental values are found to be in close agreement. This establishes the use of ANN in developing prediction models for better monitoring of the condition of a machine tool for enhancing the productivity
Authors and Affiliations
Lokesha , P B Nagaraj , Pushpalatha M N , P Dinesh
Keywords
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