Prediction of Wear Mechanisms in High Speed Steel Hobs Using Artificial Neural network

摘要

In this paper back-propagation artificial neural network (BPANN) is employed to predict the wear of the gear hobbing tools. The wear of high speed steel hobs during hobbing has been studied. The wear mechanisms are strongly influenced by the choice of cutting speed. At moderate and high cutting speeds three major wear mechanisms were identified: abrasion, mild adhesive and severe adhesive. The microstructure and wear behavior of two high speed steel grades (M2 and ASP30) has been compared, hi contrast, a variation in chemical composition or microstructure of HSS tool material generally did not change the dominant wear mechanism. However, the tool material properties determine the resistance against the operating wear mechanism and consequently the tool life. The metallographic analysis and wear measurement at the tip of hob teeth included scanning electron microscopy and stereoscope microscopy. Comparing experimental and BPANN results, an acceptable correlation was found.