RT Journal Article ID 0cd25cf071c1b95f A1 Sahoo , Sucharita Sanghamitra A1 Rout, Arun Kumar T1 PREDICTION OF EROSION WEAR OF GRANITE-FILLED JUTE−EPOXY COMPOSITES USING AN ARTIFICIAL NEURAL NETWORK JF Composites: Mechanics, Computations, Applications: An International Journal JO CMCA YR 2015 FD 2015-07-01 VO 6 IS 3 SP 193 OP 205 K1 erosion wear K1 artificial neural network K1 experimental design K1 granite powder K1 jute fiber AB The erosion wear process is considered as a complex nonlinear problem due to its operating variables. The wear process depends on various parameters such as impact velocity, impingement angle, material, erodent size, etc. In order to obtain a minimum erosion rate, experiments have to be conducted on a material with a combination of these parameters. Therefore, it becomes expensive and time consuming for finding out the minimum erosion rate of a material. In this regard, an artificial neural network (ANN) is a robust tool for predicting the erosion rate of a material. ANN is capable of representing nonlinear systems and can be applied to a wide variety of fields. In the present work, waste granite powder is used as a filler in a jute fiber-reinforced−epoxy composite. Four different weight proportions of granite (0, 5, 10, and 15 wt.%) are used to fabricate four different composites. It is observed that the predicted values of erosion rate have shown good agreement with experimental values. PB Begell House LK https://www.dl.begellhouse.com/journals/36ff4a142dec9609,301ad67e105ef06e,0cd25cf071c1b95f.html