Journal
MEASUREMENT
Volume 98, Issue -, Pages 25-34Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2016.11.027
Keywords
Surface roughness; ANN; RSM; Turning
Funding
- National Basic Research Program of China (973 Program) [2013CB035805]
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This paper demonstrates a nested-ANN (Artificial Neural Network) mddel predicting surface roughness (Ra). The special ANN includes enclosed-ANNs and an output-ANN. The enclosed-ANN models use cutting parameters as inputs to predict the values of cutting forces and tool vibrations respectively, and then forward all outputs to the output-ANN model. Subsequently, the output-ANN adopts the forward values and cutting parameters as inputs to predict R. To verify the effectiveness of the nested-ANN model, it is compared with mathematical and statistical models based on conventional ANN and RSM (Response Surface Methodology) using the same experimental data. The results show that the nested-ANN uses less input variables to obtain superior prediction accuracy than other models. Additionally, the statistical analyses show that Ra is mostly affected by the feed rate and has a signification correlation with the feed rate, the cutting force in both radial and tangential directions as well as the tool vibrations. (C) 2016 Elsevier Ltd. All rights reserved.
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