A particle swarm optimisation-based grey prediction model for thermal error compensation on CNC machine tools

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Thermal errors can have a significant effect on CNC machine tool accuracy. The thermal error compensation system has become a cost-effective method of improving machine tool accuracy in recent years. In the presented paper, the Grey relational analysis (GRA) was employed to obtain the similarity degrees between fixed temperature sensors and the thermal response of the CNC machine tool structure. Subsequently, a new Grey model with convolution integral GMC(1, N) is used to design a thermal prediction model. To improve the accuracy of the proposed model, the generation coefficients of GMC(1, N) are calibrated using an adaptive Particle Swarm Optimisation (PSO) algorithm. The results demonstrate good agreement between the experimental and predicted thermal error. Finally, the capabilities and the limitations of the model for thermal error compensation have been discussed.

LanguageEnglish
Title of host publicationLaser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015
Publishereuspen
Pages363-372
Number of pages10
ISBN (Electronic)9780956679055
Publication statusPublished - 2015
Event11th International Conference and Exhibition on Laser Metrology, Coordinate Measuring Machine and Machine Tool Performance - , United Kingdom
Duration: 17 Mar 201518 Mar 2015
Conference number: 11

Conference

Conference11th International Conference and Exhibition on Laser Metrology, Coordinate Measuring Machine and Machine Tool Performance
Abbreviated titleLAMDAMAP 2015
CountryUnited Kingdom
Period17/03/1518/03/15

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machine tools
optimization
predictions
temperature sensors
convolution integrals
costs
coefficients

Cite this

Abdulshahed, A. M., Longstaff, A. P., & Fletcher, S. (2015). A particle swarm optimisation-based grey prediction model for thermal error compensation on CNC machine tools. In Laser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015 (pp. 363-372). euspen.
Abdulshahed, Ali M. ; Longstaff, Andrew P. ; Fletcher, Simon. / A particle swarm optimisation-based grey prediction model for thermal error compensation on CNC machine tools. Laser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015. euspen, 2015. pp. 363-372
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title = "A particle swarm optimisation-based grey prediction model for thermal error compensation on CNC machine tools",
abstract = "Thermal errors can have a significant effect on CNC machine tool accuracy. The thermal error compensation system has become a cost-effective method of improving machine tool accuracy in recent years. In the presented paper, the Grey relational analysis (GRA) was employed to obtain the similarity degrees between fixed temperature sensors and the thermal response of the CNC machine tool structure. Subsequently, a new Grey model with convolution integral GMC(1, N) is used to design a thermal prediction model. To improve the accuracy of the proposed model, the generation coefficients of GMC(1, N) are calibrated using an adaptive Particle Swarm Optimisation (PSO) algorithm. The results demonstrate good agreement between the experimental and predicted thermal error. Finally, the capabilities and the limitations of the model for thermal error compensation have been discussed.",
author = "Abdulshahed, {Ali M.} and Longstaff, {Andrew P.} and Simon Fletcher",
year = "2015",
language = "English",
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booktitle = "Laser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015",
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Abdulshahed, AM, Longstaff, AP & Fletcher, S 2015, A particle swarm optimisation-based grey prediction model for thermal error compensation on CNC machine tools. in Laser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015. euspen, pp. 363-372, 11th International Conference and Exhibition on Laser Metrology, Coordinate Measuring Machine and Machine Tool Performance, United Kingdom, 17/03/15.

A particle swarm optimisation-based grey prediction model for thermal error compensation on CNC machine tools. / Abdulshahed, Ali M.; Longstaff, Andrew P.; Fletcher, Simon.

Laser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015. euspen, 2015. p. 363-372.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - A particle swarm optimisation-based grey prediction model for thermal error compensation on CNC machine tools

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AU - Longstaff, Andrew P.

AU - Fletcher, Simon

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N2 - Thermal errors can have a significant effect on CNC machine tool accuracy. The thermal error compensation system has become a cost-effective method of improving machine tool accuracy in recent years. In the presented paper, the Grey relational analysis (GRA) was employed to obtain the similarity degrees between fixed temperature sensors and the thermal response of the CNC machine tool structure. Subsequently, a new Grey model with convolution integral GMC(1, N) is used to design a thermal prediction model. To improve the accuracy of the proposed model, the generation coefficients of GMC(1, N) are calibrated using an adaptive Particle Swarm Optimisation (PSO) algorithm. The results demonstrate good agreement between the experimental and predicted thermal error. Finally, the capabilities and the limitations of the model for thermal error compensation have been discussed.

AB - Thermal errors can have a significant effect on CNC machine tool accuracy. The thermal error compensation system has become a cost-effective method of improving machine tool accuracy in recent years. In the presented paper, the Grey relational analysis (GRA) was employed to obtain the similarity degrees between fixed temperature sensors and the thermal response of the CNC machine tool structure. Subsequently, a new Grey model with convolution integral GMC(1, N) is used to design a thermal prediction model. To improve the accuracy of the proposed model, the generation coefficients of GMC(1, N) are calibrated using an adaptive Particle Swarm Optimisation (PSO) algorithm. The results demonstrate good agreement between the experimental and predicted thermal error. Finally, the capabilities and the limitations of the model for thermal error compensation have been discussed.

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Abdulshahed AM, Longstaff AP, Fletcher S. A particle swarm optimisation-based grey prediction model for thermal error compensation on CNC machine tools. In Laser Metrology and Machine Performance XI - 11th International Conference and Exhibition on Laser Metrology, Machine Tool, CMM and Robotic Performance, LAMDAMAP 2015. euspen. 2015. p. 363-372