Artificial neural network based prediction of drill flank wear from motor current signals

Patra, Karali; Pal, Surjya K.; Bhattacharyya, K.

doi:10.1016/j.asoc.2006.06.001

Cited by 59 publications

(23 citation statements)

References 23 publications

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“…However, the use of ANNs to classify the state of the tools is widespread because of their adaptive learning, self-organization, fault tolerance and real-time operation, providing good solutions to classifi cation or decision making problems. 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).…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

ANN Based Tool Condition Monitoring System for CNC Milling Machines

Mota-Valtierra

Franco-Gasca

Herrera-Ruíz

et al. 2011

IIT

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“…TCM systems that are found to be suitable and more reliable for continuous machining operations and research mainly focused on turning operation [5], but they are not accurate for semi or fully-intermittent machining, like grinding or milling [6]. It was found that the process parameters need major tuning for precise detection of tool wear and the estimation of tool life [7]. For effective TCM the chosen parameters are very important and an incorrect choice could lead to a poor response [2].…”

Section: Introductionmentioning

confidence: 99%

Understanding Acoustic Emission for Different Metal Cutting Machinery and Operations

Nikhare

Conklin

Loker

2017

JMMP

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Abstract:Machining is one of the major manufacturing techniques where the material is removed to prepare the complete or sub-part. In general, this is also referred to as subtractive manufacturing. Due to solid-to-solid contact between the cutting tool and the work-piece, the machine dynamics get influenced by various operating parameters. This generates force and vibration, and thus noise. Over time the cutting tool reaches its end-of-life which increases the force to cut, and thus produces more vibration and noise. The noise parameter was considered in this work. A 32-element spherical microphone array acoustic camera system was used to record and analyze the sound that was emitted during the machining processes. The startup, idle, and load operating characteristics for various industrial machining equipment were monitored with the acoustic beam former microphone system. The industrial applications included a bench grinder, surface grinder, vertical band saw, lathe machine, and vertical milling machine. Analysis of the acoustic noise generated from these processes could demonstrate the similarities between the cyclical patterns of resonating sound.

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“…Jemielniak et al [14] has studied the various signals for vibration, force, and acoustic emission while turning a specific material and extracted some basic features from the different domains such as frequency, time, and time frequency domains of the signals to detect the wear of the tool. Karali Patra [15] has developed artificial neural network based prediction using the motor current signals of drill flank wear. So far very few attempts have been made to generate the online tool wear condition monitoring system using the spindle motor current signal especially in milling machine.…”

Section: Introductionmentioning

confidence: 99%

Online Tool Wear Prediction in Milling Operation Using Feed Motor Current Signal

2016

Arimpie - 2016

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Abstract-In the present work, artificial neural network (ANN) and regression analysis are used to predict the real time flank wear of a cutting tool (KC710) in milling operation. The various parameters such as root mean square (RMS) value of the spindle motor current, spindle speed, depth of cut and feed-rate are the inputs to the network, and flank wear is the output. Online data of milling operation is taken for training and testing of the neural networks. Effect of various cutting conditions (speed, depth of cut, feed-rate) on the flank wear has been investigated. The flank wear prediction using the trained network is found to be in better agreement in comparison with the experimentally measured flank wear of the milling tool. The value of co-efficient of determination (R 2 ), between the model predictions and the experimentally obtained value are compared. Accuracy of the predicted flank wear using neural network is found to be better than that using regression model.

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Artificial neural network based prediction of drill flank wear from motor current signals

Cited by 59 publications

References 23 publications

ANN Based Tool Condition Monitoring System for CNC Milling Machines

ANN Based Tool Condition Monitoring System for CNC Milling Machines

Understanding Acoustic Emission for Different Metal Cutting Machinery and Operations

Online Tool Wear Prediction in Milling Operation Using Feed Motor Current Signal

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