Multi-sensor and MTConnect dataset of metal cutting anomaly in milling from laboratory and industry settings.

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Bibliographic Details
Title: Multi-sensor and MTConnect dataset of metal cutting anomaly in milling from laboratory and industry settings.
Authors: Kim E; Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, USA. kim3235@purdue.edu., Sim Y; School of Mechanical Engineering, Purdue University, West Lafayette, USA., Li AS; Department of Computer Science, Purdue University, West Lafayette, USA., Mostafiz MI; Department of Computer Science, Purdue University, West Lafayette, USA., Van Meter Z; TMF Center, Williamsport, USA., Jun MB; School of Mechanical Engineering, Purdue University, West Lafayette, USA., Bertino E; Department of Computer Science, Purdue University, West Lafayette, USA., Shakouri A; Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, USA.
Source: Scientific data [Sci Data] 2026 Apr 24. Date of Electronic Publication: 2026 Apr 24.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101640192 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2052-4463 (Electronic) Linking ISSN: 20524463 NLM ISO Abbreviation: Sci Data Subsets: MEDLINE
Database: MEDLINE Ultimate
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ISSN:2052-4463
DOI:10.1038/s41597-026-07255-7