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  4. Experiment Data for the Publication: A Novel Data Concept for Cutting Processes through Comprehensive Experimental Setup enabling Grey-box Models
 
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Experiment Data for the Publication: A Novel Data Concept for Cutting Processes through Comprehensive Experimental Setup enabling Grey-box Models

Citation Link: https://doi.org/10.15480/882.15882
Type
Experimental Data
Version
1.0
Date Issued
2025-09-15
Author(s)
Schibsdat, Sebastian  
Produktionsmanagement und -technik M-18  
Researcher
Dege, Jan Hendrik  orcid-logo
Produktionsmanagement und -technik M-18  
Contact
Dege, Jan Hendrik  orcid-logo
Produktionsmanagement und -technik M-18  
Möller, Carsten  
Produktionsmanagement und -technik M-18  
Language
English
DOI
https://doi.org/10.15480/882.15882
TORE-URI
https://hdl.handle.net/11420/57409
Used equipment
Digitalmikroskop Keyence VHX-7000N  
Gildemeister Max MĂĽller MD5S  
Optimizer4D Hochfrequenz-Impuls-Messsystem  
3-Komponenten Dynamometer Typ 9257B  
Faseroptisches Zwei-Farben Pyrometer FIRE-3  
Acceleration sensor W356A03 NC, PCB Piezotronics
scattered light sensor OS 500, OptoSurf GmbH
Abstract
The dataset consists of several wear tests and is supplementary to the paper 'Special Issue Wear: A Novel Data Concept for Cutting Processes through Comprehensive Experimental Setup Enabling Grey-Box Models'. The experiments cover two different cutting edges of various indexable inserts, each of which was used with constant process parameters until failure. The following data is available as in-situ online measurements: Three-axis force and acceleration; structure-borne noise; and rake face temperature. In-situ offline data includes scattered light from the workpiece surface (Aq and macro profile angle) and microscope images of the cutting edge with extraced heightmap(clearance face, rake face and cutting edge).
Subjects
Wear
Turning
Indexable Insert
In-Situ Measurement
DDC Class
621: Applied Physics
Funding(s)
Extrapolationsfähige digitale Greybox-Modelle zur Beschreibung und Vorhersage des makroskopischen Systemverhaltens TiAlN-beschichteter Zerspanwerkzeuge  
Funding Organisations
Deutsche Forschungsgemeinschaft (DFG)  
More Funding Information
This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 521385417 (SPP 2402 - Greybox models for the qualification of coated tools for high-performance cutting, subproject D2: Extrapolative digital greybox models for describing and predicting the macroscopic system behavior of TiAlN-coated cutting tools).
License
https://creativecommons.org/publicdomain/mark/1.0/
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Experiment_Documentation.xlsx

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13.21 KB

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Microsoft Excel XML

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Heightmaps.tar

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1.16 GB

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tar

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Images.tar

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28.59 MB

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tar

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Experiment_Data.tar

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4.03 GB

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tar

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readme.txt

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1.94 KB

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Text

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