Estimation of tool wear during CNC milling using neural network-based sensor fusion

Ghosh, Nirmalya; Ravi, Yapatla; Patra, Aditya Kumar; Mukhopadhyay, Sudipta; Paul, S.; Mohanty, A.R.; Chattopadhyay, Angshuman

doi:10.1016/j.ymssp.2005.10.010

Cited by 340 publications

(131 citation statements)

References 26 publications

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“…The neural networks can be used to estimate an output from one or more input and a target output [8]- [10]. The generation stage of the NN gives the following results (Figs.…”

Section: Change Of Machining Direction Change Of Machining Directionmentioning

confidence: 99%

Neural Network Monitoring Strategy of Cutting Tool Wear of Horizontal High Speed Milling

Kious

Aissa

Ameur

et al. 2014

AMM

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Abstract-The wear of cutting tool degrades the quality of the product in the manufacturing processes. The on line monitoring of the cutting tool wear level is very necessary to prevent the deterioration of the quality of machining. Unfortunately there is not a direct manner to measure the cutting tool wear on line. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission etc…. In this work, a neural network system is elaborated in order to estimate the flank wear from the cutting force measurement and the cutting conditions.

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“…The neural networks can be used to estimate an output from one or more input and a target output [8]- [10]. The generation stage of the NN gives the following results (Figs.…”

Section: Change Of Machining Direction Change Of Machining Directionmentioning

confidence: 99%

Neural Network Monitoring Strategy of Cutting Tool Wear of Horizontal High Speed Milling

Kious

Aissa

Ameur

et al. 2014

AMM

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“…Nevertheless, when the costs of these systems are reduced, it is likely to use them in many measurements such as tool wear, cutting force, Acoustic Emission (AE) and vibrations [6]. Ghosh et al [7] focused on the prediction of tool wear in CNC milling using sensors in integration with neural networks. In their study, they found that the average flank wear of the main cutting edge was predicted by the signals such as cutting force, cutting tool vibration, and sound pressure level obtained from the machining region.…”

Section: Introductionmentioning

confidence: 99%

Determination of Surface Qualities on Inclined Surface Machining with Acoustic Sound Pressure

Gok

2012

SV-JME

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“…The ones most commonly used are artificial neural networks (ANN) due to their features, such as abilities of identification of complex systems and processes, parallel data processing, noise suppression characteristics, and adaptability to varying machining conditions and tool wear dynamics (Wang et al 2001). Among a number of neural network types, the most frequently used are Multilayer Perceptron Neural Network-MLP NN (Sick 2002), commonly trained by the Error-Back Propagation algorithm (Huang & Chen 2000;Tandon & El-Mounayri 2001;Chen & Chen 2004;Ghosh et al 2007;Alonso & Salgado 2008). Besides them, TCM systems built on Radial Basis Function NN (Srinivasa et al 2002), Adaptive…”

Section: Introductionmentioning

confidence: 99%

Tool wear estimation using an analytic fuzzy classifier and support vector machines

et al. 2010

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A new type of continuous hybrid tool wear estimator is proposed in this paper. It is structured in the form of two modules for classification and estimation. The classification module is designed by using an analytic fuzzy logic concept without a rule base. Thereby, it is possible to utilize fuzzy logic decision-making without any constraints in the number of tool wear features in order to enhance the module robustness and accuracy. The final estimated tool wear parameter value is obtained from the estimation module. It is structured by using a support vector machine nonlinear regression algorithm. The proposed estimator implies the usage of a larger number and various types of features, which is in line with the concept of a closer integration between machine tools and different types of sensors for tool condition monitoring.

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Estimation of tool wear during CNC milling using neural network-based sensor fusion

Cited by 340 publications

References 26 publications

Neural Network Monitoring Strategy of Cutting Tool Wear of Horizontal High Speed Milling

Neural Network Monitoring Strategy of Cutting Tool Wear of Horizontal High Speed Milling

Determination of Surface Qualities on Inclined Surface Machining with Acoustic Sound Pressure

Tool wear estimation using an analytic fuzzy classifier and support vector machines

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