TSK fuzzy modeling for tool wear condition in turning processes: An experimental study

Ren, Qun; Balazinski, Marek; Baron, Luc; Jemielniak, Krzysztof

doi:10.1016/j.engappai.2010.10.016

Cited by 43 publications

(19 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2. This algorithm is presented in [39,40]. Subtractive clustering method combined with a least-square estimation algorithm is used to cope with the nonlinearity of the AE signal and the uncertainty of imprecise data from measurement.…”

Section: Fuzzy Modeling Algorithmmentioning

confidence: 99%

Fuzzy identification of cutting acoustic emission with extended subtractive cluster analysis

2011

Self Cite

View full text Add to dashboard Cite

This paper presents fuzzy acoustic emission identification in high precision hard turning process based on extended subtractive cluster analysis combined with the least-square estimation method. The fuzzy identification method provides a simple way to arrive at a definite conclusion based upon the information obtained with the difficulty in understanding the exact physics of the machining process. The experimental results prove that the proposed method is efficient and feasible.

show abstract

Section: Fuzzy Modeling Algorithmmentioning

confidence: 99%

Fuzzy identification of cutting acoustic emission with extended subtractive cluster analysis

2011

Self Cite

View full text Add to dashboard Cite

show abstract

“…2, subtractive clustering, combined with least-square estimation, accomplish the integration of multi-sensor information to identify a fuzzy model. The detailed description can be found in [11,12,14].…”

Section: Fuzzy Identification Algorithmmentioning

confidence: 99%

Fuzzy cutting force modelling in micro-milling

Ren

Baron

Balazinskia

et al. 2013

Journal of Intelligent &Amp; Fuzzy Systems

Self Cite

View full text Add to dashboard Cite

Reliable prediction of cutting forces is essential for micromilling. In this paper, a fuzzy cutting force modelling method based on subtractive clustering method filters the noise and estimates the instantaneous cutting forces using observations acquired by sensors during cutting experiments. In the experimental case study, four data sets of micromilling cutting force are used. Each data set is used to generate a learning system which is tested by the other three data sets. It is proven that the proposed algorithm has the capability to filter and model the cutting force in spite of uncertainties in the micromilling process.

show abstract

“…The generality of the TSK FLSs makes data driven identification very complex. In this paper, extended subtractive -clustering [26] is used for fuzzy system structure identification and least square estimation is used for parameter identification. The former is the determination of the number of rules and variables involved in the rule premises, while the latter is the estimation of the membership function parameters and the estimation of the consequent regression coefficients.…”

Section: Fuzzy Logic Controlmentioning

confidence: 99%

A Highly Accurate Model-Free Motion Control System with a Mamdani Fuzzy Feedback Controller combined with a TSK Fuzzy Feed-forward Controller

Ren

Bigras

2017

J Intell Robot Syst

Self Cite

View full text Add to dashboard Cite

show abstract

TSK fuzzy modeling for tool wear condition in turning processes: An experimental study

Cited by 43 publications

References 11 publications

Fuzzy identification of cutting acoustic emission with extended subtractive cluster analysis

Fuzzy identification of cutting acoustic emission with extended subtractive cluster analysis

Fuzzy cutting force modelling in micro-milling

A Highly Accurate Model-Free Motion Control System with a Mamdani Fuzzy Feedback Controller combined with a TSK Fuzzy Feed-forward Controller

Contact Info

Product

Resources

About