{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T21:00:11Z","timestamp":1785445211923,"version":"3.56.0"},"reference-count":50,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T00:00:00Z","timestamp":1686960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The milling machine serves an important role in manufacturing because of its versatility in machining. The cutting tool is a critical component of machining because it is responsible for machining accuracy and surface finishing, impacting industrial productivity. Monitoring the cutting tool\u2019s life is essential to avoid machining downtime caused due to tool wear. To prevent the unplanned downtime of the machine and to utilize the maximum life of the cutting tool, the accurate prediction of the remaining useful life (RUL) cutting tool is essential. Different artificial intelligence (AI) techniques estimate the RUL of cutting tools in milling operations with improved prediction accuracy. The IEEE NUAA Ideahouse dataset has been used in this paper for the RUL estimation of the milling cutter. The accuracy of the prediction is based on the quality of feature engineering performed on the unprocessed data. Feature extraction is a crucial phase in RUL prediction. In this work, the authors considers the time\u2013frequency domain (TFD) features such as short-time Fourier-transform (STFT) and different wavelet transforms (WT) along with deep learning (DL) models such as long short-term memory (LSTM), different variants of LSTN, convolutional neural network (CNN), and hybrid models that are a combination of CCN with LSTM variants for RUL estimation. The TFD feature extraction with LSTM variants and hybrid models performs well for the milling cutting tool RUL estimation.<\/jats:p>","DOI":"10.3390\/s23125659","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T02:29:19Z","timestamp":1687141759000},"page":"5659","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time\u2013Frequency-Based Features and Deep Learning Models"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5895-5350","authenticated-orcid":false,"given":"Sameer","family":"Sayyad","sequence":"first","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6788-0952","authenticated-orcid":false,"given":"Satish","family":"Kumar","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India"},{"name":"Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1942-9179","authenticated-orcid":false,"given":"Arunkumar","family":"Bongale","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2653-3780","authenticated-orcid":false,"given":"Ketan","family":"Kotecha","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India"},{"name":"Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0169-6738","authenticated-orcid":false,"given":"Ajith","family":"Abraham","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Data Science, Flame University, Lavale, Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1007\/s10845-019-01500-0","article-title":"Real-time machining data application and service based on IMT digital twin","volume":"31","author":"Tong","year":"2019","journal-title":"J. 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