Prediction and analysis of surface roughness characteristics of a non-ferrous material using ANN in CNC turning

Surface roughness, an indicator of surface quality is one of the most-specified customer requirements in a machining process. For efficient use of machine tools, optimum cutting parameters (speed, feed, and depth of cut) are required. So it is necessary to find a suitable optimization method which c...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2011-12, Vol.57 (9-12), p.1043-1051
Main Authors: Natarajan, Chinnasamy, Muthu, S., Karuppuswamy, P.
Format: Article
Language:English
Subjects:
Artificial neural networks
Back propagation networks
CAE) and Design
Computer-Aided Engineering (CAD
Cutting parameters
Cutting speed
Engineering
Industrial and Production Engineering
Lathes
Machine tools
Mechanical Engineering
Media Management
Numerical controls
Optimization
Original Article
Predictions
Process parameters
Rapid prototyping
Surface properties
Surface roughness
Turning (machining)
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Summary:Surface roughness, an indicator of surface quality is one of the most-specified customer requirements in a machining process. For efficient use of machine tools, optimum cutting parameters (speed, feed, and depth of cut) are required. So 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 non-linear. In this work, machining process has been carried out on brass C26000 material in dry cutting condition in a CNC turning machine and surface roughness has been measured using surface roughness tester. To predict the surface roughness, an artificial neural network (ANN) model has been designed through feed-forward back-propagation network using Matlab (2009a) software for the data obtained. Comparison of the experimental data and ANN results show that there is no significant difference and ANN has been used confidently. The results obtained conclude that ANN is reliable and accurate for predicting the values. The actual R a value has been obtained as 1.1999 μm and the corresponding predicted surface roughness value is 1.1859 μm, which implies greater accuracy.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-011-3343-1