On-line metal cutting tool condition monitoring.

Dimla, Eric; Lister, P.M.

doi:10.1016/s0890-6955(99)00084-x

Cited by 314 publications

(89 citation statements)

References 11 publications

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“…Correspondingly, of course, many scoring models applying multilayer feed‐forward nets have been used (Trippi and Turban, ; Bishop, ; Desai et al , ; Reed and Marks, ; West, ; Dimla and Lister, ). The neural network models have the highest ACC rates in these studies when compared with discriminant analysis and logistic regression, or other techniques, although results are often very close.…”

Section: Review Of the Literaturementioning

confidence: 99%

Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature

Abdou

Pointon

2011

Intell. Sys. Acc. Fin. Mgmt.

306

209

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SummaryCredit scoring has been regarded as a core appraisal tool of different institutions during the last few decades, and has been widely investigated in different areas, such as finance and accounting. Different scoring techniques are being used in areas of classification and prediction, where statistical techniques have conventionally been used. Both sophisticated and traditional techniques, as well as performance evaluation criteria are investigated in the literature. The principal aim of this paper is to carry out a comprehensive review of 214 articles/books/theses that involve credit scoring applications in various areas, in general, but primarily in finance and banking, in particular. This paper also aims to investigate how credit scoring has developed in importance, and to identify the key determinants in the construction of a scoring model, by means of a widespread review of different statistical techniques and performance evaluation criteria. Our review of literature revealed that there is no overall best statistical technique used in building scoring models and the best technique for all circumstances does not yet exist. Also, the applications of the scoring methodologies have been widely extended to include different areas, and this subsequently can help decision makers, particularly in banking, to predict their clients" behaviour. Finally, this paper also suggests a number of directions for future research.

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Section: Review Of the Literaturementioning

confidence: 99%

Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature

Abdou

Pointon

2011

Intell. Sys. Acc. Fin. Mgmt.

306

209

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“…Choudhury and Sharath [13] pointed out that the vibration of cutting process was one of the major factors limiting its performance and provided an on-line control system of machine tool vibration during turning operation. Dimla Sr. and Lister [14] investigated the on-line metal-cutting tool condition monitoring by vibration analyses and reported that the analyses in time and frequency domains showed some components of the measured signals to correlate well with the accrued tool wear.…”

Section: Machining Vibrationmentioning

confidence: 99%

Effect of rare earth oxide on the properties of laser cladding layer and machining vibration suppressing in side milling

Zhao

Sun

2014

Applied Surface Science

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“…Dimla and Lister [4] developed an online tool wear monitoring system. They used tool dynamometer to measure three vertical components of the cutting force and the tool wear state monitoring was studied through the analyses of time series and fast Fourier transform (FFT) of the signals.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Turning Tool Monitoring with Neural Network Adaptive Learning

Wang

et al. 2019

Complexity

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Tool state monitoring is a key technology in intelligent manufacturing. But it is still in a research stage and lacks general adaptability for different machining conditions. To overcome this limitation, this work systematically investigates an intelligent, real-time, and visible tool state monitoring through adopting integrated theories and technologies, i.e., (a) through distinctively designed experimental technique with comprehensive consideration of cutting parameters and tool wear values as variables, (b) through bisensor fusion for simultaneous measurements of low and high frequency signals, (c) through multitheory fusion of wavelet decomposition and the Relief-F algorithm for performing dual feature extraction and feature dimension reduction to achieve more accurate state identification using neural network, and (d) through an innovative programming technique of MATLAB-nested labVIEW. This tool monitoring system has neural network adaptive learning ability with the change of the experimental sample signals which are collected simultaneously by selected vibration and acoustic emission (AE) sensors in all factors turning experiments. Based on LabVIEW and MATLAB hybrid programming, the waveforms of signals are observed and the significant characteristics of signals are extracted through the time-frequency domain analysis and then the calculation of the energy proportion of each frequency band obtained using 4 levels of wavelet packet decompositions of the vibration signal as well as 8 levels of wavelet multiresolution decompositions of the AE signal; the ensuing Relief-F algorithm is used to further reextract the features that are most relevant to the tool state as input of neural network pattern recognition. Through the BP neural network adaptive learning, tool state recognition model is finally established. The results show that the correct recognition rate of BP network model after samples training is 92.59%, which can more accurately and intelligently monitor the tool wear state.

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On-line metal cutting tool condition monitoring.

Cited by 314 publications

References 11 publications

Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature

Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature

Effect of rare earth oxide on the properties of laser cladding layer and machining vibration suppressing in side milling

Intelligent Turning Tool Monitoring with Neural Network Adaptive Learning

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