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Application of Levenberg- Marquardt Based Back Propagation Neurointelligence Algorithm in Studying the Cutting Parameters Effect on Thrust Force & Hole Diameteral Accuracy in Drilling of Aluminum Alloys

Hossam M. Abd El-rahman *

In this paper, the effect of the cutting speed, feed rate and the point angle, mechanical properties of aluminium alloys on diametric error and thrust force were investigated and estimated by assistance of a neural network using Levenberg- Marquardt Based Back Propagation Algorithm. Three types of commercial aluminium alloys were selected as the work piece materials for experiments. The neural network analysis were employed to analyze the effect of drilling parameters and predict the response of diametral error and the thrust force toward the drilling parameters changes. The results of network sensitivity and relative importance analysis indicated that feed rate and cutting speed minimize significantly both the diametral error and the thrust force.