Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques

Bhattacharyya, Prantik; Sengupta, Debapriya; Mukhopadhyay, Sudipta

doi:10.1016/j.ymssp.2007.01.004

Cited by 152 publications

(57 citation statements)

References 26 publications

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“…To eliminate this process, the simulation 4.7485242378162704 B 4 6.6937787138683325 B5 3.1640591505230589 B 6 18.628574291499405 B 7 -1.0110257151509474 B8 -4.9142447720322169 B 9 -10.498695451961783 B 10 5.4051552263109226 B11 13.852649299020378 B12 4.5307357794522964 B 13 5.1769721889169595 B 14 -6.1287801118826621 model of the developed ANN model was designed as a block diagram. In this block diagram, the cutting parameters are given in the model and the cutting forces and vibration amplitudes received from sensors are applied as the variables to the input of the simulation model.…”

Section: Results and Discussion 41 Ann Modelingmentioning

confidence: 99%

“…Zhou, Kasashima, and some other researchers used a new method that makes use of wavelet techniques to monitor the cutting process and estimate tool failure in face milling operation [5]. Bhattacharyya and Sengupta used an OLTCM method based on time features And Multiple Linear Regression (MLR) models by using force signal to introduce a statistical model [6]. These studies have gained di erent degrees of success in tool wear prediction; however, using modern methods to recognize the tool wear will increase the performance of the developed models.…”

Section: Introductionmentioning

confidence: 99%

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Applying a multi sensor system to predict and simulate the tool wear using of artificial neural networks

2017

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Abstract. Cutting tool wear in machining processes reduces the product surface quality, a ects the dimensional and geometrical tolerances, and causes tool breakage during the metal cutting. Therefore, online tool wear monitoring is needed to prevent reduction in machining quality. An Arti cial Neural Network (ANN) model was developed in this study to predict and simulate the tool ank wear. To achieve this aim, an experiment array was provided using full factorial method, and the tests were conducted on a CNC lathe machine tool. Vibration amplitude of the cutting tool and cutting forces were considered as criterion variables in monitoring the tool ank wear. For designing the model, the cutting parameters, cutting forces, and vibration amplitude were de ned as model inputs, and tool ank wear was selected as an output. The model was also introduced as a simulation block diagram to be used as a useful model in online and automated manufacturing systems. The estimated and measured results were then compared with each other. Based on the comparison results, maximum squared error values are under 6 10 14 mm, and R 2 is 1, meaning that the designed model can predict the results with high and reliable accuracy.

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Section: Results and Discussion 41 Ann Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying a multi sensor system to predict and simulate the tool wear using of artificial neural networks

2017

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“…5 & 6. Using such statistical parameters as mean, Root Mean Square and variance, it can be observed that the variance values provided more relevant information on the evolution of the milling cutter wear than the values of other parameters [5], [6]. World The variance evolution (Fig.…”

Section: A Temporal Analysismentioning

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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“…Examples of ANNs applied to the tool condition classifi cation may be found in Rivero (2008), Mehrabian et al (2008), Patra et al (2007) and Kuljanic et al (2009). Jantunen (2002) reported a summary of methods used to detect breakage and wear of the cutt ing tools, showing that cutt ing forces are commonly used to classify the tool wear, also, this fact may be appreciated in works presented by Kuljanic et al (2005), Rivero et al (2008), Bhatt acharyya et al (2007) and Jemielniak et al (2008). In order to evaluate the cutt ing forces, Zuperl et al (2004) and Kurt (2009) developed simulation models, these models determine the cutt ing forces with more precision than the analytical models due to the application of Multi Layer Perceptron (MLP) type ANNs.…”

Section: Introductionmentioning

confidence: 95%

ANN Based Tool Condition Monitoring System for CNC Milling Machines

Mota-Valtierra

Franco-Gasca

Herrera-Ruíz

et al. 2011

IIT

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Sistema basado en redes neuronales artificiales para el monitoreo de la herramienta en fresadoras CNC AbstractMost of the companies have as objective to manufacture high-quality products, then by optimizing costs, reducing and controlling the variations in its production processes it is possible. Within manufacturing industries a very important issue is the tool condition monitoring, since the tool state will determine the quality of products. Besides, a good monitoring system will protect the machinery from severe damages. For determining the state of the cutt ing tools in a milling machine, there is a great variety of models in the industrial market, however these systems are not available to all companies because of their high costs and the requirements of modifying the machining tool in order to att ach the system sensors. This paper presents an intelligent classifi cation system which determines the status of cutt ers in a Computer Numerical Control (CNC) milling machine. This tool state is mainly detected through the analysis of the cutt ing forces drawn from the spindle motors currents. This monitoring system does not need sensors so it is no necessary to modify the machine. The correct classifi cation is made by advanced digital signal processing techniques. Just after acquiring a signal, a FIR digital fi lter is applied to the data to eliminate the undesired noisy components and to extract the embedded force components. A Wavelet Transformation is applied to the fi ltered signal in order to compress the data amount and to optimize the classifi er structure. Then a multilayer perceptron-type neural network is responsible for carrying out the classifi cation of the signal. Achieving a reliability of 95%, the system is capable of detecting breakage and a worn cutt er.

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Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques

Cited by 152 publications

References 26 publications

Applying a multi sensor system to predict and simulate the tool wear using of artificial neural networks

Applying a multi sensor system to predict and simulate the tool wear using of artificial neural networks

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

ANN Based Tool Condition Monitoring System for CNC Milling Machines

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