Prediction Flank Wear of Carbide Cutting Tool Using Back Propagation Artificial Neural Network

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Chalermpol Klaynil
Pongsakorn Leetrakul


This research aimed to study the Influence of turning parameters for AISI 316 austenitic stainless steel and AISI 420 martensitic stainless steel, which affected on flank wear of CVD coated carbide insert. Minitab was used for design of experiment and the statistics analysis. The study proposed the model to predict the flank wear values using artificial neural network with the back-propagation, and trained with Levenberg-Marquardt (trainlm). The input parameters were cutting speed, feed rate and depth of cut, Output was tool flank wear. The results showed that the main effect and interactions gave a significant effect on the flank wear. The result from using of back-propagation artificial neural network to predict the flank wear found that mean square error (MSE) from training equal 1.23414e-4, the testing was 1.50536e-4 for the machining process of AISI 316 austenitic stainless steel. The MSE of machining process for AISI 420 martensitic stainless steel from training equal1.45619 e-4 as the testing was 1.95559e-4. It can predict flank wear accurately and very low errors, which will be able to make production plan properly and to reduce cutting tools that affects the cost of production.


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Klaynil, C., & Leetrakul, P. (2019). Prediction Flank Wear of Carbide Cutting Tool Using Back Propagation Artificial Neural Network. Naresuan University Engineering Journal, 14(2), 37–48. Retrieved from
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