A novel approach for ANFIS modelling based on Grey system theory for thermal error compensation

Ali M. Abdulshahed; Andrew P. Longstaff; Simon Fletcher

doi:10.1109/UKCI.2014.6930155

A novel approach for ANFIS modelling based on Grey system theory for thermal error compensation

Ali M. Abdulshahed, Andrew P. Longstaff, Simon Fletcher

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

The fast and accurate modelling of thermal errors in machining is an important aspect for the implementation of thermal error compensation. This paper presents a novel modelling approach for thermal error compensation on CNC machine tools. The method combines the Adaptive Neuro Fuzzy Inference System (ANFIS) and Grey system theory to predict thermal errors in machining. Instead of following a traditional approach, which utilises original data patterns to construct the ANFIS model, this paper proposes to exploit Accumulation Generation Operation (AGO) to simplify the modelling procedures. AGO, a basis of the Grey system theory, is used to uncover a development tendency so that the features and laws of integration hidden in the chaotic raw data can be sufficiently revealed. AGO properties make it easier for the proposed model to design and predict. According to the simulation results, the proposed model demonstrates stronger prediction power than standard ANFIS model only with minimum number of training samples.

Original language	English
Title of host publication	2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479955381
DOIs	https://doi.org/10.1109/UKCI.2014.6930155
Publication status	Published - 17 Oct 2014
Event	14th UK Workshop on Computational Intelligence - University of Bradford, Bradford, United Kingdom Duration: 8 Sep 2014 → 10 Sep 2014 Conference number: 14 http://www.computing.brad.ac.uk/ukci2014/ (Link to Conference Website ) https://ieeexplore.ieee.org/xpl/conhome/6917611/proceeding

Workshop

Workshop	14th UK Workshop on Computational Intelligence
Abbreviated title	UKCI 2014
Country/Territory	United Kingdom
City	Bradford
Period	8/09/14 → 10/09/14
Internet address	http://www.computing.brad.ac.uk/ukci2014/ (Link to Conference Website ) https://ieeexplore.ieee.org/xpl/conhome/6917611/proceeding

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title = "A novel approach for ANFIS modelling based on Grey system theory for thermal error compensation",

abstract = "The fast and accurate modelling of thermal errors in machining is an important aspect for the implementation of thermal error compensation. This paper presents a novel modelling approach for thermal error compensation on CNC machine tools. The method combines the Adaptive Neuro Fuzzy Inference System (ANFIS) and Grey system theory to predict thermal errors in machining. Instead of following a traditional approach, which utilises original data patterns to construct the ANFIS model, this paper proposes to exploit Accumulation Generation Operation (AGO) to simplify the modelling procedures. AGO, a basis of the Grey system theory, is used to uncover a development tendency so that the features and laws of integration hidden in the chaotic raw data can be sufficiently revealed. AGO properties make it easier for the proposed model to design and predict. According to the simulation results, the proposed model demonstrates stronger prediction power than standard ANFIS model only with minimum number of training samples.",

keywords = "Adaptive Neuro Fuzzy Inference System, Grey system theory, thermal errors",

author = "Abdulshahed, {Ali M.} and Longstaff, {Andrew P.} and Simon Fletcher",

year = "2014",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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note = "14th UK Workshop on Computational Intelligence, UKCI 2014 ; Conference date: 08-09-2014 Through 10-09-2014",

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Abdulshahed, AM, Longstaff, AP & Fletcher, S 2014, A novel approach for ANFIS modelling based on Grey system theory for thermal error compensation. in 2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings., 6930155, Institute of Electrical and Electronics Engineers Inc., 14th UK Workshop on Computational Intelligence, Bradford, United Kingdom, 8/09/14. https://doi.org/10.1109/UKCI.2014.6930155

A novel approach for ANFIS modelling based on Grey system theory for thermal error compensation. / Abdulshahed, Ali M.; Longstaff, Andrew P.; Fletcher, Simon.

2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. 6930155.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A novel approach for ANFIS modelling based on Grey system theory for thermal error compensation

AU - Abdulshahed, Ali M.

AU - Longstaff, Andrew P.

AU - Fletcher, Simon

N1 - Conference code: 14

PY - 2014/10/17

Y1 - 2014/10/17

N2 - The fast and accurate modelling of thermal errors in machining is an important aspect for the implementation of thermal error compensation. This paper presents a novel modelling approach for thermal error compensation on CNC machine tools. The method combines the Adaptive Neuro Fuzzy Inference System (ANFIS) and Grey system theory to predict thermal errors in machining. Instead of following a traditional approach, which utilises original data patterns to construct the ANFIS model, this paper proposes to exploit Accumulation Generation Operation (AGO) to simplify the modelling procedures. AGO, a basis of the Grey system theory, is used to uncover a development tendency so that the features and laws of integration hidden in the chaotic raw data can be sufficiently revealed. AGO properties make it easier for the proposed model to design and predict. According to the simulation results, the proposed model demonstrates stronger prediction power than standard ANFIS model only with minimum number of training samples.

AB - The fast and accurate modelling of thermal errors in machining is an important aspect for the implementation of thermal error compensation. This paper presents a novel modelling approach for thermal error compensation on CNC machine tools. The method combines the Adaptive Neuro Fuzzy Inference System (ANFIS) and Grey system theory to predict thermal errors in machining. Instead of following a traditional approach, which utilises original data patterns to construct the ANFIS model, this paper proposes to exploit Accumulation Generation Operation (AGO) to simplify the modelling procedures. AGO, a basis of the Grey system theory, is used to uncover a development tendency so that the features and laws of integration hidden in the chaotic raw data can be sufficiently revealed. AGO properties make it easier for the proposed model to design and predict. According to the simulation results, the proposed model demonstrates stronger prediction power than standard ANFIS model only with minimum number of training samples.

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KW - Grey system theory

KW - thermal errors

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BT - 2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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A novel approach for ANFIS modelling based on Grey system theory for thermal error compensation

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