Journal
MATERIALS TESTING
Volume 59, Issue 10, Pages 916-920Publisher
CARL HANSER VERLAG
DOI: 10.3139/120.111088
Keywords
Magnesium alloy; AZ91D; artificial neural network; surface roughness; property prediction
Ask authors/readers for more resources
This contribution presents an approach for the modeling and prediction of surface roughness in the turning of AZ91D magnesium alloys using an artificial neural network. The experiments were conducted with CCGT, DCGT and VCGT cutting tools under minimum quantity lubrication and dry machining conditions. AZ91D alloys were machined at different cutting speeds and feed rates, and the depth of cut was kept constant. 15 out of 18 experimental data points were used for the training of the artificial neural network model and the remaining 3 were used for the testing process. The average percentage error was calculated as 0.000815 % and 0.663 % for training and testing, respectively. The model and target results were found to have extremely low error rates.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available