Abstract. Nowadays, finishing operation in hardened steel parts which have wide industrial applications is done by hard turning. Cubic boron nitride (CBN) inserts, which are expensive, are used for hard turning. The cheaper coated carbide tool is seen as a substitute for CBN inserts in the hardness range (45-55 HRC). However, tool wear in a coated carbide tool during hard turning is a significant factor that influences the tolerance of machined surface. An online tool wear estimation system is essential for maintaining the surface quality and minimizing the manufacturing cost. In this investigation, the cutting tool wear estimation using artificial neural network (ANN) is proposed. AISI4140 steel hardened to 47 HRC is used as a work piece and a coated carbide tool is the cutting tool. Experimentation is based on full factorial design (FFD) as per design of experiments. The variations in cutting forces and vibrations are measured during the experimentation. Based on the process parameters and measured parameters an ANN-based tool wear estimator is developed. The wear outputs from the ANN model are then tested. It was observed that as the model using ANN provided quite satisfactory results, and that it can be used for online tool wear estimation.Key words: hard turning, coated carbide, cutting force, vibration, ANN.Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool cutting forces, tool dynamometers are widely used. Another prominent feature used in TCM is the vibration signal. The amplitude of the vibration signal in the dynamic frequency band of the tool holder's natural frequency along the z direction is more profound to wear, and it can be considered a feature for TCM [15][16][17].Since tool wear is a complex phenomenon, the signal information from a single sensor is inadequate to predict the wear accurately. Hence, it is advisable to employ multiple sensors. The highlight of the multi-sensor system is the abundance of information available, which can be used for decision making. Many researchers have used a combination of force and vibration signals, as well as acoustic emission signals, to monitor tool wear and roughness [18][19][20].The estimation of tool wear from the sensor signals is performed by developing a mathematical model from the experimental data, referred to as the regression equation. Since the relationship between features from sensors and tool wear are nonlinear, the regression equation may not hold well. The artificial neural networks (ANN) using a mapping technique between the input and output are extensively employed [21][22][23] whenever the relation is nonlinear. The selection of input parameters, hidden layer, and inner error depend upon the cutting process in ANN.The main research factor in hard turning is the estimation wear in coated carbide cutting tool which can be used as a replacement for expensive CBN tools. Even though many investigations have been carried out on wear estimation and TCM, the research wor...

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