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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Hard turning has replaced conventional grinding in production processes in recent years as an emerging technique. Nowadays, coated carbide tools are replacing expensive CBN inserts in turning. Wear is a significant concern when turning with coated carbide; it immediately affects the acceptability of the machined surface, which causes machine downtime and loss due to wastage in machined parts. Online tool condition monitoring (TCM) is required to prevent such critical conditions. Hard turning differs from conventional turning in energy balance during metal cutting, resulting in greater thrust force; hence, the TCM model presented for conventional turning may not be suitable for hard turning. Hence, tool wear prediction for turning is projected based on thrust force using an artificial neural network (ANN). All of the tests were done using a design of experiments called full factorial design (FFD). The specimens were made of AISI 4140 steel that had been hardened to 47 HRC, and the inserts were made of coated carbide. The most impactful input features for wear, selected based on experimental outputs, were given to the neural network and trained. Tool wear is an estimated output from the training set that has been validated with satisfactory results for random conditions. The 5–10–1 network structure with the Levenberg–Marquardt (LM) learning algorithm, R2 values of 0.996602 and 0.969437 for the training and testing data, and mean square error values of 0.000133152 and 0.004443 for the training and testing data, respectively, gave the best results. The MEP values of 0.575407 and 2.977617 are very low (5%). The LM learning algorithm-based ANN is good at predicting tool wear based on how well it predicts tool wear for both the testing set and the training set.

On-line monitoring of tool wear in turning is vital to increase machine utilization as scrapped components, machine tool breakage and unscheduled downtime result from worn tool usage cause huge economic loss. Several techniques have been developed for monitoring wear levels on the cutting tool on-line. Keeping in to account the difficulties encountered during the implementation of tool condition monitoring (TCM). The signal acquisition is one of the key elements used during the implementation of TCM. This paper provides an in depth coverage of various signal acquisition methods used in TCM.

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