{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T19:00:06Z","timestamp":1743102006409,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031197611"},{"type":"electronic","value":"9783031197628"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19762-8_17","type":"book-chapter","created":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T08:03:09Z","timestamp":1666166589000},"page":"227-234","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Using Model Selection and Reduction to Develop an Empirical Model to Predict Energy Consumption of a CNC Machine"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7740-8579","authenticated-orcid":false,"given":"Liam","family":"Morris","sequence":"first","affiliation":[]},{"given":"Rose","family":"Clancy","sequence":"additional","affiliation":[]},{"given":"Andriy","family":"Hryshchenko","sequence":"additional","affiliation":[]},{"given":"Dominic","family":"O\u2019Sullivan","sequence":"additional","affiliation":[]},{"given":"Ken","family":"Bruton","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"17_CR1","unstructured":"IEA, Key World Energy Statistics 2021, Paris (2021).\u00a0\u00a0https:\/\/www.iea.org\/reports\/key-world-energy-statistics-2021\/final-consumption. Accessed 26 Jul\u00a02022"},{"key":"17_CR2","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1016\/j.procir.2013.06.045","volume":"7","author":"K Salonitis","year":"2013","unstructured":"Salonitis, K., Ball, P.: Energy efficient manufacturing from machine tools to manufacturing systems. Procedia CIRP 7, 634\u2013639 (2013). https:\/\/doi.org\/10.1016\/j.procir.2013.06.045","journal-title":"Procedia CIRP"},{"key":"17_CR3","doi-asserted-by":"publisher","unstructured":"Mulrennan, K., Donovan, J., D. Tormey, D., Macpherson, R.: A data science approach to modelling a manufacturing facility\u2019s electrical energy profile from plant production data. In:\u00a02018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), \u00a0pp. 387\u2013391 (2018). https:\/\/doi.org\/10.1109\/DSAA.2018.00050","DOI":"10.1109\/DSAA.2018.00050"},{"key":"17_CR4","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.cirpj.2015.08.001","volume":"11","author":"A Cataldo","year":"2015","unstructured":"Cataldo, A., Scattolini, R., Tolio, T.: An energy consumption evaluation methodology for a manufacturing plant. CIRP J. Manuf. Sci. Technol. 11, 53\u201361 (2015). https:\/\/doi.org\/10.1016\/j.cirpj.2015.08.001","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"17_CR5","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1016\/j.promfg.2018.02.173","volume":"21","author":"J Reimann","year":"2018","unstructured":"Reimann, J., Wenzel, K., Friedemann, M., Putz, M.: Methodology and model for predicting energy consumption in manufacturing at multiple scales. Procedia Manuf. 21, 694\u2013701 (2018). https:\/\/doi.org\/10.1016\/j.promfg.2018.02.173","journal-title":"Procedia Manuf."},{"key":"17_CR6","unstructured":"European Union H2020, DENiM: Digital intelligence for collaborative ENergy management in Manufacturing.\u00a0https:\/\/denim-fof.eu\/. \u00a0Accessed 15 Aug\u00a02022"},{"key":"17_CR7","doi-asserted-by":"publisher","first-page":"12078","DOI":"10.1088\/1757-899x\/346\/1\/012078","volume":"346","author":"GK Garg","year":"2018","unstructured":"Garg, G.K., Garg, S., Sangwan, K.S.: Development of an empirical model for optimization of machining parameters to minimize power consumption. IOP Conf. Ser. Mater. Sci. Eng. 346, 12078 (2018). https:\/\/doi.org\/10.1088\/1757-899x\/346\/1\/012078","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"issue":"1","key":"17_CR8","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.cirp.2012.03.103","volume":"61","author":"T Behrendt","year":"2012","unstructured":"Behrendt, T., Zein, A., Min, S.: Development of an energy consumption monitoring procedure for machine tools. CIRP Ann. 61(1), 43\u201346 (2012). https:\/\/doi.org\/10.1016\/j.cirp.2012.03.103","journal-title":"CIRP Ann."},{"issue":"1","key":"17_CR9","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.jmsy.2013.12.004","volume":"33","author":"T Peng","year":"2014","unstructured":"Peng, T., Xu, X., Wang, L.: A novel energy demand modelling approach for CNC machining based on function blocks. J. Manuf. Syst. 33(1), 196\u2013208 (2014). https:\/\/doi.org\/10.1016\/j.jmsy.2013.12.004","journal-title":"J. Manuf. Syst."},{"key":"17_CR10","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.jclepro.2017.04.096","volume":"157","author":"IF Edem","year":"2017","unstructured":"Edem, I.F., Mativenga, P.T.: Modelling of energy demand from computer numerical control (CNC) toolpaths. J. Clean. Prod. 157, 310\u2013321 (2017). https:\/\/doi.org\/10.1016\/j.jclepro.2017.04.096","journal-title":"J. Clean. Prod."},{"key":"17_CR11","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.procir.2014.07.072","volume":"26","author":"G Kant","year":"2015","unstructured":"Kant, G., Sangwan, K.S.: Predictive modeling for power consumption in machining using artificial intelligence techniques. Procedia CIRP 26, 403\u2013407 (2015). https:\/\/doi.org\/10.1016\/j.procir.2014.07.072","journal-title":"Procedia CIRP"},{"key":"17_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2021.104423","volume":"89","author":"RH Fard","year":"2022","unstructured":"Fard, R.H., Hosseini, S.: Machine Learning algorithms for prediction of energy consumption and IoT modeling in complex networks. Microprocess. Microsyst. 89, 104423 (2022). https:\/\/doi.org\/10.1016\/j.micpro.2021.104423","journal-title":"Microprocess. Microsyst."},{"issue":"1","key":"17_CR13","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1080\/21693277.2016.1192517","volume":"4","author":"T Wuest","year":"2016","unstructured":"Wuest, T., Weimer, D., Irgens, C., Thoben, K.-D.: Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4(1), 23\u201345 (2016). https:\/\/doi.org\/10.1080\/21693277.2016.1192517","journal-title":"Prod. Manuf. Res."},{"key":"17_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2021.106953","volume":"117","author":"P Sun","year":"2021","unstructured":"Sun, P., et al.: Evaluation of applicability of empirical models of turbine performance to aircraft engine. Aerosp. Sci. Technol. 117, 106953 (2021). https:\/\/doi.org\/10.1016\/j.ast.2021.106953","journal-title":"Aerosp. Sci. Technol."},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Militk\u00fd, J.: 3 - Fundamentals of soft models in textiles. In: Majumdar, A. (ed.) Soft Computing in Textile Engineering. A volume in Woodhead Publishing Series in Textiles, pp. 45\u2013102. Woodhead Publishing (2011)","DOI":"10.1533\/9780857090812.1.45"},{"issue":"1","key":"17_CR16","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.cirp.2011.03.018","volume":"60","author":"S Kara","year":"2011","unstructured":"Kara, S., Li, W.: Unit process energy consumption models for material removal processes. CIRP Ann. - Manuf. Technol. 60(1), 37\u201340 (2011). https:\/\/doi.org\/10.1016\/j.cirp.2011.03.018","journal-title":"CIRP Ann. - Manuf. Technol."},{"issue":"9-12","key":"17_CR17","doi-asserted-by":"publisher","first-page":"3345","DOI":"10.1007\/s00170-016-8441-7","volume":"86","author":"RS Alt\u0131nta\u015f","year":"2016","unstructured":"Alt\u0131nta\u015f, R.S., Kahya, M., \u00dcnver, H.\u00d6.: Modelling and optimization of energy consumption for feature based milling. Int. J. Adv. Manuf. Technol. 86(9\u201312), 3345\u20133363 (2016). https:\/\/doi.org\/10.1007\/s00170-016-8441-7","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"17_CR18","doi-asserted-by":"publisher","unstructured":"Adeniji D., Schoop J., In-situ calibrated digital process Twin models for resource efficient manufacturing. J. Manuf. Sci. Eng. 144, 4 (2021). doi: https:\/\/doi.org\/10.1115\/1.4052131","DOI":"10.1115\/1.4052131"},{"issue":"9","key":"17_CR19","doi-asserted-by":"publisher","first-page":"2356","DOI":"10.1016\/j.enbuild.2011.05.020","volume":"43","author":"P Raftery","year":"2011","unstructured":"Raftery, P., Keane, M., O\u2019Donnell, J.: Calibrating whole building energy models: an evidence-based methodology. Energy Build. 43(9), 2356\u20132364 (2011). https:\/\/doi.org\/10.1016\/j.enbuild.2011.05.020","journal-title":"Energy Build."}],"container-title":["Lecture Notes in Computer Science","Leveraging Applications of Formal Methods, Verification and Validation. Practice"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19762-8_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T23:15:00Z","timestamp":1666221300000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19762-8_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197611","9783031197628"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19762-8_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISoLA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Leveraging Applications of Formal Methods","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rhodes","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isola2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.isola-conference.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}