Development of an ANN model for prediction of tool wear in turning EN9 and EN24 steel alloy

An imperative requirement of a modern machining system is to detect tool wear while maintain the surface quality product. Vibration signatures emanating during with single point cutting have proven be good indicators for tool’s health. The current research undertaken utilizes vibration turning EN9 and EN24 steel alloy predict life using Artificial Neural Network (ANN). During initial meager experimentation, acceleration was recorded, width flank at end each run measured Tool Makers Microscope. recorded experimental data utilized develop neural network variation operating parameters corresponding wear. endeavor development ANN prediction model effective regression coefficient 0.9964. proposed methodology indirect measurement efficient, economical industry life, which in turn avoids catastrophic failure.