PREDICTION OF WORK PIECE HARDNESS USING ARTIFICIAL NEURAL NETWORK
Balamuruga Mohan Raj G., Vaithiyanathan Sugumaran · INTERNATIONAL JOURNAL OF DESIGN AND MANUFACTURING TECHNOLOGY · 2010
In a machining operation, the productivity depends on the work-tool combination, speed, feed and depth of cut etc.Among the properties of the work-tool materials combination, hardness plays a crucial role in machining.To select the appropriate parameters including the tool material to meet the above objectives, the hardness of the workpiece is to be known.In small and medium scale machining industries the job orders will be of different materials with varying hardness values.This demands an online hardness measuring system.The methodology proposed in this paper is to measure the feed motor current using a current sensor and relate it with the hardness of the material to be machined.This is achieved using a tool with high hardness as a pilot machining tool with standard speed, feed and depth of cut.An artificial neural network (ANN) model has been built to predict hardness using spindle motor current.The ANN has been optimized to predict best possible values and the result of the same has been compared with that of regression analysis.