Tool wear monitoring using naïve Bayes classifiers

Karandikar, Jaydeep; McLeay, Thomas E.; Turner, Sam; Schmitz, Tony L.

doi:10.1007/s00170-014-6560-6

Cited by 96 publications

(45 citation statements)

References 26 publications

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“…Furthermore, it also works well with the noisy data [80]. The Naïve Bayes are also an efficient and effective machine learning classifier [81][82][83], while Softmax classifier is one of the most commonly-used logistic regressions classifier especially for multi-class classification [84,85]. Finally, the SVM classifier are also selected due to it is well established technique in many image recognition tasks and its high accuracy performance [78,[86][87][88][89].…”

Section: Classificationmentioning

confidence: 99%

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

et al. 2019

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Smart manufacturing optimizes productivity with the integration of computer control and various high level adaptability technologies including the big data evolution. The evolution of big data offers optimization through data analytics as a predictive solution in future planning decision making. However, this requires accurate and reliable informative data as input for analytics. Therefore, in this paper, the fusion features for apple classification is investigated to classify between defective and non-defective apple for automatic inspection, sorting and further predictive analytics. The fusion features with Decision Tree classifier called Curvelet Wavelet-Gray Level Co-occurrence Matrix (CW-GLCM) is designed based on symmetrical pattern. The CW-GLCM is tested on two apple datasets namely NDDA and NDDAW with a total of 1110 apple images. Each dataset consists of a binary class of apple which are defective and non-defective. The NDDAW consists more low-quality region images. Experimental results show that CW-GLCM successfully classify 98.15% of NDDA dataset and 89.11% of NDDAW dataset. A lower classification accuracy is observed in other five existing image recognition methods especially on NDDAW dataset. Finally, the results show that CW-GLCM is more accurate among all the methods with the difference of more than 10.54% of classification accuracy.

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Section: Classificationmentioning

confidence: 99%

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

et al. 2019

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“…One of classification methods was Naive Bayes classifier. It was well described in the literature [48,60]. This classifier used a posterior probability, which was expressed by (2):…”

Section: Bayes Classifiermentioning

confidence: 99%

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Głowacz¹,

Głowacz²,

Głowacz³

et al. 2017

Archives of Metallurgy and Materials

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A degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies -Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

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“…Our case study involves tool condition monitoring for metal cutting processes. In manufacturing, it is important to monitor tool wear, as tool breakages can result in unscheduled machine downtime in a factory, whilst excessive wear can cause poor quality and the scrapping of the finished part for failing to meet specifications, both of which can result in significant financial loss . Clearly, there is a trade‐off, as changing tools too soon would drive up tooling costs unnecessarily.…”

Section: Case Studymentioning

confidence: 99%

“…scrapping of the finished part for failing to meet specifications, both of which can result in significant financial loss. 2 Clearly, there is a trade-off, as changing tools too soon would drive up tooling costs unnecessarily. The specific machining operation of interest in our case study is 'milling' where a metal workpiece is fed past a rotating cylindrical tool with multiple cutting edges, which are called flutes or teeth.…”

Section: Case Studymentioning

confidence: 99%

A Multivariate Control Chart for Autocorrelated Tool Wear Processes

Harris

Triantafyllopoulos

Stillman

et al. 2016

Quality & Reliability Eng

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Full automation of metal cutting processes has been a long held goal of the manufacturing industry. One key obstacle to achieving this ambition has been the inability to monitor completely the condition of the cutting tool in real time, as premature tool breakage and heavy tool wear can result in substantial costs through damage to the machinery and increasing the risk of non-conforming items that have to be scrapped or reworked. Instead, the condition of the tool has to be indirectly monitored using modern sensor technology that measures the acoustic emission, sound, spindle power and vibration of the tool during a cut. An online monitoring procedure for such data is proposed. Firstly, the standard deviation is extracted from each sensor signal to summarise the state of the tool after each cut. Secondly, a multivariate autoregressive state space model is specified for estimating the joint effects and cross-correlation of the sensor variables in Phase I. Then we apply a distributionfree monitoring scheme to the model residuals in Phase II, based on binomial type statistics. The proposed methodology is illustrated using a case study of titanium alloy milling (a machining process used in the manufacture of aircraft landing gears) from the Advanced Manufacturing Research Centre in Sheffield, UK, and is demonstrated to outperform alternative residual control charts in this application.

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Tool wear monitoring using naïve Bayes classifiers

Cited by 96 publications

References 26 publications

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

A Multivariate Control Chart for Autocorrelated Tool Wear Processes

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