A cuckoo search optimisation-based Grey prediction model for thermal error compensation on CNC machine tools

Ali M. Abdulshahed, Andrew Longstaff, Simon Fletcher

Research output: Contribution to journalArticle

Abstract

Purpose:
The purpose of this paper is to produce an intelligent technique for modelling machine tool errors caused by the thermal distortion of Computer Numerical Control (CNC) machine tools. A new metaheuristic method, the cuckoo search (CS) algorithm, based on the life of a bird family is proposed to optimize the GMC(1, N) coefficients. It is then used to predict thermal error on a small vertical milling centre based on selected sensors.

Design/methodology/approach:
A Grey model with convolution integral GMC(1, N) is used to design a thermal prediction model. To enhance the accuracy of the proposed model, the generation coefficients of GMC(1, N) are optimized using a new metaheuristic method, called the CS algorithm.

Findings:
The results demonstrate good agreement between the experimental and predicted thermal error. It can therefore be concluded that it is possible to optimize a Grey model using the CS algorithm, which can be used to predict the thermal error of a CNC machine tool.

Originality/value:
An attempt has been made for the first time to apply CS algorithm for calibrating the GMC(1, N) model. The proposed CS-based Grey model has been validated and compared with particle swarm optimization (PSO) based Grey model. Simulations and comparison show that the CS algorithm outperforms PSO and can act as an alternative optmization algorithm for Grey models that can be used for thermal error compensation.
Original languageEnglish
Pages (from-to)146-155
Number of pages10
JournalGrey Systems
Volume7
Issue number2
DOIs
Publication statusPublished - 7 Aug 2017

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Error compensation
Machine tools
Particle swarm optimization (PSO)
Hot Temperature
Birds
Convolution
Sensors

Cite this

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abstract = "Purpose:The purpose of this paper is to produce an intelligent technique for modelling machine tool errors caused by the thermal distortion of Computer Numerical Control (CNC) machine tools. A new metaheuristic method, the cuckoo search (CS) algorithm, based on the life of a bird family is proposed to optimize the GMC(1, N) coefficients. It is then used to predict thermal error on a small vertical milling centre based on selected sensors.Design/methodology/approach:A Grey model with convolution integral GMC(1, N) is used to design a thermal prediction model. To enhance the accuracy of the proposed model, the generation coefficients of GMC(1, N) are optimized using a new metaheuristic method, called the CS algorithm.Findings:The results demonstrate good agreement between the experimental and predicted thermal error. It can therefore be concluded that it is possible to optimize a Grey model using the CS algorithm, which can be used to predict the thermal error of a CNC machine tool.Originality/value:An attempt has been made for the first time to apply CS algorithm for calibrating the GMC(1, N) model. The proposed CS-based Grey model has been validated and compared with particle swarm optimization (PSO) based Grey model. Simulations and comparison show that the CS algorithm outperforms PSO and can act as an alternative optmization algorithm for Grey models that can be used for thermal error compensation.",
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A cuckoo search optimisation-based Grey prediction model for thermal error compensation on CNC machine tools. / Abdulshahed, Ali M.; Longstaff, Andrew; Fletcher, Simon.

In: Grey Systems, Vol. 7, No. 2, 07.08.2017, p. 146-155.

Research output: Contribution to journalArticle

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