Support Vector Regression approach for prediction of delamination at entry and exit during drilling of GFRP Composites

The demand for Composites in the modern era have increased immensely due to its vast applications and superior properties over conventional materials. Glass Fibre Reinforced Plastic (GFRP) is one of economic alternative engineering materials high specific modulus elasticity, strength, good corrosion resistance, fatigue strength lightweight. Components made out from GFRP composites are usually near net shaped require holes assembly integration. Drilling an important process as concentrated forces can cause major damage composite. causes various such thermal degradation, fibre breakage, matrix cracking delamination. A substantial caused by delamination which occur both on entry exit sides composite, side considered more severe. Therefore, selection proper parameters during drilling operation very much essential. In present work, a support vector regression (SVR) model developed predict at composites. based data obtained experimentation. accuracy evaluated three performance criteria including root mean square error (RMSE), Nash–Sutcliffe efficiency co-efficient (E) determination (R2). provides inexpensive time saving study composite actual operation.