Author(s): A.S. El-Akkad , T. H. Sayed, M. M. Koura
Published in: International Journal of Engineering Research & Technology
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume/Issue: Vol. 3 - Issue 7 (July - 2014)
Surface roughness (𝑹𝒂) prediction model using artificial neural network (ANN) is developed in this work for dry turning of mild steel (St. 42) using carbide inserts. Cutting parameters (cutting speed, feed rate, and depth of cut) are used as network inputs. Also, investigations of the effect of cutting parameters are presented. The analysis reveals that increasing the cutting speed will decrease surface roughness, and increasing feed rate will increase surface roughness. The results show that the developed ANN model can predict surface roughness with regression value 98.641 % between measured and predicted values and average error 5.4%. Detailed experimentation and ANN network structure are presented in the paper.
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