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Monitoring and predicting the wear condition of a cutting tool is key to guarantee the cutting quality and saving costs. This study presents an AI-driven digital twin framework for real-time tool life prediction to address these limitations by integrating multiple modules. These modules include an on-machine direct inspection system, a seamless connectivity integration module for real-time data management, and a deep learning module for tool wear prediction. Long Short-Term Memory networks were trained, optimised and tested on a milling dataset to then deploy onto a real-time implementation of the digital twin framework. A comprehensive design of experiments (DOE) was used to validate the real-time tool life prediction framework of a dynamic milling toolpath strategy of a Ti-6Al-4\u00a0V alloy. The models were able to predict tool maximum flank wear based on sensor data from the machining tests DOE with RMSE of 33.17\u00a0\u00b5m, whilst the real-time implementation yielded a minimum of RMSE of 119.36\u00a0\u00b5m. These results motivate further research for enabling real-time closed-loop control for a future digital twin system implementation.<\/jats:p>","DOI":"10.1007\/s10845-025-02606-4","type":"journal-article","created":{"date-parts":[[2025,4,20]],"date-time":"2025-04-20T23:11:57Z","timestamp":1745190717000},"page":"1491-1511","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Intelligent real-time tool life prediction for a digital twin framework"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7329-9929","authenticated-orcid":false,"given":"Javier","family":"Dominguez-Caballero","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1899-9743","authenticated-orcid":false,"given":"Sabino","family":"Ayvar-Soberanis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6402-6996","authenticated-orcid":false,"given":"David","family":"Curtis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"issue":"4\u20135","key":"2606_CR1","doi-asserted-by":"publisher","first-page":"3419","DOI":"10.1016\/J.MATPR.2015.07.317","volume":"2","author":"N Ambhore","year":"2015","unstructured":"Ambhore, N., Kamble, D., Chinchanikar, S., & Wayal, V. 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