Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system

Rizal, Muhammad; Ghani, Jaharah A; Nuawi, Mohd. Zaki; Haron, Che Hassan Che

doi:10.1016/j.asoc.2012.11.043

Cited by 95 publications

(36 citation statements)

References 32 publications

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“…The cutting conditions tested using cutting speeds range as in simulation that are 125 -200 m/min, the feed rate and depth of cut were kept constant at 0.25 mm/rev and 1.5 mm respectively. The cutting force was measured using newly developed in house cutting force measurement system which consists of two channels strain gauge, data acquisitions system and personal computer [13].…”

Section: Experimental Used For Fem Results Validationmentioning

confidence: 99%

Performance of Uncoated and Coated Carbide Tools in Turning FCD700 Using FEM Simulation

Yanda¹,

Ghani²,

Rizal³

2015

Int. j. simul. model.

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This paper presents investigations on the performance of uncoated and multi-layer coated carbide tools while turning ductile cast iron FCD700. Three different squares-edged carbide tools were used, namely TiN+TiCN+Al 2 O 3 +TiN-coated carbide tools, multi-layered hard coating of 5 and 10 µm thickness and an uncoated WC/Co tool. Deform-3D FEM software is utilised to predict the main cutting force, the sliding velocity, interface temperature and interface pressure and tool wear rate on these inserts. The results show that the main cutting forces obtained with multi-layer carbide tools are smaller than the uncoated carbide tool. The simulated results for cutting forces were validated experimentally, and the maximum error between the simulation and experimental results is 8.14 %. It was found that the coated carbide tool with the highest thickness is the most suitable for turning ductile cast iron at higher cutting speeds.

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Section: Experimental Used For Fem Results Validationmentioning

confidence: 99%

Performance of Uncoated and Coated Carbide Tools in Turning FCD700 Using FEM Simulation

Yanda¹,

Ghani²,

Rizal³

2015

Int. j. simul. model.

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“…In the current manufacturing environment, indirect manufacturing operations generate direct costs that can be avoided or reduced by using control systems [5,6]. The use of Intelligent Manufacture Systems (IMS) has been researched through the application of Artificial Neural Networks (ANNs) since 1980 [7].…”

Section: Introductionmentioning

confidence: 99%

Correcting geometric deviations of CNC Machine-Tools: An approach with Artificial Neural Networks

Leite

Rubio

Duduch

et al. 2015

Applied Soft Computing

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“…But tool wear in carbide inserts is higher, leading to machine downtime. It is reported that the tool wear constitutes about 20% of the total downtime, leading to a drastic increase in production costs [5,6]. To prevent such critical situation, the status of tool wear during machining should be known.…”

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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Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system

Cited by 95 publications

References 32 publications

Performance of Uncoated and Coated Carbide Tools in Turning FCD700 Using FEM Simulation

Performance of Uncoated and Coated Carbide Tools in Turning FCD700 Using FEM Simulation

Correcting geometric deviations of CNC Machine-Tools: An approach with Artificial Neural Networks

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

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