LAUSR

Titanium alloys are most widely used in aerospace, military, marine, medical and bio-medical industries because of their unique properties such as high corrosion resistance, biocompatibility, superior strength-to-weight ratio and ability to withstand at elevated temperatures. In spite of the above mentioned capabilities these alloys are classified as “Difficult-to-machine” type materials. This is due to lower thermal conductivity and high chemical affinity of these materials. Poor thermal conductivity and increased chemical reactivity during titanium machining causes rapid tool wear. This might be contributed to the high heat generated at primary deformation zone. This situation necessitates proper selection of the machining parameter as well as cutting tool material before machining the workpiece. The present paper suggests a novel approximation tool for predicting some of the vital machinability aspects during the turning of commercially pure titanium grade 2. Experiments were conducted at five different cutting speeds (30, 60, 90, 120 and 150 m/min) with a constant feed rate of 0.12 mm/rev and depth of cut of 0.5 mm. The variations attained in the performance measures viz. Surface roughness (Ra), cutting force (Fc) and flank wear (VBc) were graphically analyzed. A generalized regression neural network (GRNN) model was developed for the prediction of aforesaid output responses. The neural network was trained, validated and tested with the experimentally measured data sets. The grid search method was employed to find the optimal sigma – value (kernel bandwidth) with minimum cross-validation error. The obtained results were validated and tested in order to demonstrate the adequacy of the model. The results indicated that the GRNN model is successfully capable of approximating turning quality characteristics within ± 5% error.