{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T06:03:34Z","timestamp":1777183414047,"version":"3.51.4"},"reference-count":18,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2017,8,7]],"date-time":"2017-08-07T00:00:00Z","timestamp":1502064000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["GS"],"published-print":{"date-parts":[[2017,8,7]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>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, <jats:italic>N<\/jats:italic>) coefficients. It is then used to predict thermal error on a small vertical milling centre based on selected sensors.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>A Grey model with convolution integral GMC(1, <jats:italic>N<\/jats:italic>) is used to design a thermal prediction model. To enhance the accuracy of the proposed model, the generation coefficients of GMC(1, <jats:italic>N<\/jats:italic>) are optimized using a new metaheuristic method, called the CS algorithm.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>An attempt has been made for the first time to apply CS algorithm for calibrating the GMC(1, <jats:italic>N<\/jats:italic>) 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.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/gs-08-2016-0021","type":"journal-article","created":{"date-parts":[[2017,7,26]],"date-time":"2017-07-26T07:12:37Z","timestamp":1501053157000},"page":"146-155","source":"Crossref","is-referenced-by-count":12,"title":["A cuckoo search optimisation-based Grey prediction model for thermal error compensation on CNC machine tools"],"prefix":"10.1108","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7630-0606","authenticated-orcid":false,"given":"Ali M.","family":"Abdulshahed","sequence":"first","affiliation":[]},{"given":"Andrew P.","family":"Longstaff","sequence":"additional","affiliation":[]},{"given":"Simon","family":"Fletcher","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020120612015106300_ref001","first-page":"369","article-title":"A particle swarm optimisation-based grey prediction model for thermal error compensation on CNC machine tools","year":"2015"},{"issue":"1","key":"key2020120612015106300_ref002","first-page":"158","article-title":"The application of ANFIS prediction models for thermal error compensation on CNC machine tools","volume":"27","year":"2015","journal-title":"Applied Soft Computing"},{"issue":"1","key":"key2020120612015106300_ref003","first-page":"130","article-title":"Thermal error modelling of a gantry-type 5-axis machine tool using a grey neural network model","volume":"41","year":"2016","journal-title":"Journal of Manufacturing Systems"},{"issue":"5","key":"key2020120612015106300_ref004","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/S0167-6911(82)80025-X","article-title":"Control problems of grey systems","volume":"1","year":"1982","journal-title":"Systems & Control Letters"},{"key":"key2020120612015106300_ref005","doi-asserted-by":"crossref","unstructured":"Guerrero, M., Castillo, O. and Garc\u00eda, M. (2015), \u201cCuckoo search via l\u00e9vy flights and a comparison with genetic algorithms\u201d, in Castillo, O. and Melin, P. (Eds), Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics, Springer International Publishing, Cham, pp. 91-103.","DOI":"10.1007\/978-3-319-10960-2_6"},{"key":"key2020120612015106300_ref006","first-page":"18","article-title":"A comparison of particle swarm optimization and the genetic algorithm","year":"2005"},{"issue":"4","key":"key2020120612015106300_ref007","doi-asserted-by":"crossref","first-page":"7898","DOI":"10.1016\/j.eswa.2008.11.004","article-title":"Forecasting the output of integrated circuit industry using genetic algorithm based multivariable grey optimization models","volume":"36","year":"2009","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"key2020120612015106300_ref010","first-page":"391","article-title":"A rolling grey model optimized by particle swarm optimization in economic prediction","volume":"32","year":"2014","journal-title":"Computational Intelligence"},{"key":"key2020120612015106300_ref008","volume-title":"Grey Systems: Theory and Applications","year":"2010"},{"key":"key2020120612015106300_ref009","first-page":"1","article-title":"A brief introduction to grey systems theory","year":"2011"},{"key":"key2020120612015106300_ref011","unstructured":"Longstaff, A.P., Fletcher, S. and Ford, D.G. (2003), \u201cYear. Practical experience of thermal testing with reference to ISO 230 Part 3\u201d, in Dg, F. (Ed.), Laser Metrology and Machine Performance VI, WIT Press, Southampton, pp. 473-483."},{"issue":"2","key":"key2020120612015106300_ref012","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1016\/j.cirp.2012.05.008","article-title":"Thermal issues in machine tools","volume":"61","year":"2012","journal-title":"CIRP Annals \u2013 Manufacturing Technology"},{"issue":"9","key":"key2020120612015106300_ref013","doi-asserted-by":"crossref","first-page":"4903","DOI":"10.1016\/j.amc.2011.10.055","article-title":"A research on the grey prediction model GM (1, n)","volume":"218","year":"2012","journal-title":"Applied Mathematics and Computation"},{"issue":"3","key":"key2020120612015106300_ref014","doi-asserted-by":"crossref","first-page":"36","DOI":"10.5121\/ijaia.2011.2304","article-title":"Improved cuckoo search algorithm for feedforward neural network training","volume":"2","year":"2011","journal-title":"International Journal of Artificial Intelligence & Applications"},{"key":"key2020120612015106300_ref016","volume-title":"Nature-Inspired Metaheuristic Algorithms","year":"2010"},{"key":"key2020120612015106300_ref017","volume-title":"Cuckoo Search and Firefly Algorithm","year":"2014"},{"key":"key2020120612015106300_ref018","doi-asserted-by":"crossref","unstructured":"Yang, X.-S. (2014b), \u201cCuckoo search and firefly algorithm: overview and analysis\u201d, in Yang, X.-S. (Ed.), Cuckoo Search and Firefly Algorithm, Springer, London, pp. 1-26.","DOI":"10.1007\/978-3-319-02141-6_1"},{"key":"key2020120612015106300_ref015","first-page":"210","article-title":"Cuckoo search via L\u00e9vy flights","year":"2009"}],"container-title":["Grey Systems: Theory and Application"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/GS-08-2016-0021\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/GS-08-2016-0021\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T00:46:04Z","timestamp":1753404364000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/gs\/article\/7\/2\/146-155\/86664"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,7]]},"references-count":18,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2017,8,7]]}},"alternative-id":["10.1108\/GS-08-2016-0021"],"URL":"https:\/\/doi.org\/10.1108\/gs-08-2016-0021","relation":{},"ISSN":["2043-9377"],"issn-type":[{"value":"2043-9377","type":"print"}],"subject":[],"published":{"date-parts":[[2017,8,7]]}}}