An experimental investigation on monitoring of crater wear in turning using ultrasonic technique

Dinakaran, D.; Sampathkumar, S.; Sivashanmugam, N.

doi:10.1016/j.ijmachtools.2009.08.001

Cited by 24 publications

(14 citation statements)

References 15 publications

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“…Monitoring the status of the tool is referred to as tool condition monitoring (TCM). Many authors have shown interest in TCM [7][8][9][10][11][12][13]. TCM can be done using direct, as well as indirect methods.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool

Rajeev¹,

Dinakaran²,

Singh³

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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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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Section: Introductionmentioning

confidence: 99%

Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool

Rajeev¹,

Dinakaran²,

Singh³

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

Self Cite

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“…A number of different techniques are often used to evaluate tool wear, irrespective of the surface treatment or coating used, for example optical methods [8,9], acoustic emission detection [10][11][12], ultrasonic testing [13,14], and thermal/infrared measurements [15,16]. The cost and characteristics of each technique should be considered in order to ensure the best fit for a given monitoring purpose and application.…”

Section: Introductionmentioning

confidence: 99%

Using Barkhausen noise to measure coating depth of coated high-speed steel

Seemuang

Slatter

2017

Int J Adv Manuf Technol

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Coated high-speed steel tools are widely used in machining processes as they offer an excellent tool life to cost ratio, but they quickly need replacing once the coated layer is worn away. It would be therefore useful to be able to measure the tool life remaining non-destructively and cheaply. To achieve this, the work presented here aims to measure the thickness of the coated layer of high-speed cutting tools by using Barkhausen noise (BHN) techniques. Coated highspeed steel specimens coated with two different materials (chromium nitride (CrN), titanium nitride (TiN)) were tested using a cost-effective measuring system developed for this study. Sensory features were extracted from the signal received from a pick-up coil and the signal features, Root mean square, peak count, and signal energy, were successfully correlated with the thickness of the coating layer on high-speed steel (HSS) specimens. The results suggest that the Barkhausen noise measuring system developed in this study can successfully indicate the different thickness of the coating layer on CrN/TiN coated HSS specimens.

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“…The application of the Adaptive Neural Fuzzy Inference System (ANFIS) for the modeling purpose in various different areas including machining is used a widely by researchers [3,4]. Several intelligent models for predicting as well as classifying tool life in machining operations have been developed [5,6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Modeling of Thermal Voltage Parameters for Tool Life Assessment in Face Milling

Kováč¹,

Rodić²,

Gostimirović³

et al. 2017

J. Prod. Eng.

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An experimental investigation on monitoring of crater wear in turning using ultrasonic technique

Cited by 24 publications

References 15 publications

Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool

Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool

Using Barkhausen noise to measure coating depth of coated high-speed steel

Adaptive Neuro-Fuzzy Modeling of Thermal Voltage Parameters for Tool Life Assessment in Face Milling

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