ARTIFICIAL INTELLIGENCE-BASED MODELING OF POWER CONSUMPTION IN DRY TURNING OF 42CrMo4 STEEL

Alina Ioana POPAN, Răzvan Marcel CHENDREAN, Nicolae BALC, Cosmin COSMA, Ioan Alexandru POPAN

Abstract


This study presents the development of an artificial intelligence model for predicting power consumption in the dry turning of 42CrMo4 steel. A feedforward neural network (FNN) was designed using experimental data generated through a Central Composite Design approach. The network was trained and validated in MATLAB, achieving high correlation and low prediction errors for both training and test data. Validation with practical experiments demonstrated deviations under 10%, confirming the model’s effectiveness for estimating power demand and supporting energy-efficient process optimization in CNC turning operations.


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