Determination of the Cutting-Edge Microgeometry Based on Process Forces during Peripheral Milling of Ti-6Al-4V Using Machine Learning
The residual stress state of the machined sub-surface influences the service quality indicators of a component, such as fatigue life, tribological properties, and distortion. During machining, the radius of the cutting edge changes due to tool wear. The cutting-edge rounding significantly affects th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Journal of Manufacturing and Materials Processing |
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| Online Access: | https://www.mdpi.com/2504-4494/7/3/100 |
| _version_ | 1797594007625269248 |
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| author | Matthias Wimmer Roman Hartl Michael F. Zaeh |
| author_facet | Matthias Wimmer Roman Hartl Michael F. Zaeh |
| author_sort | Matthias Wimmer |
| collection | DOAJ |
| description | The residual stress state of the machined sub-surface influences the service quality indicators of a component, such as fatigue life, tribological properties, and distortion. During machining, the radius of the cutting edge changes due to tool wear. The cutting-edge rounding significantly affects the residual stress state in the part and the occurring process forces. This paper presents a tool wear prediction model based on in-process measured cutting forces. The effects of the cutting-edge geometry on the force behavior and the machining-induced residual stresses were examined experimentally. The resulting database was used to realize a Machine Learning algorithm to calculate the hidden value of tool wear. The predictions were validated by milling experiments using rounded cutting edges for different process parameters. The microgeometry of the cutting edge could be determined with a Root Mean Square Error of 8.94 μm. |
| first_indexed | 2024-03-11T02:17:30Z |
| format | Article |
| id | doaj.art-89ac17dcedc549198ebb184867e96374 |
| institution | Directory Open Access Journal |
| issn | 2504-4494 |
| language | English |
| last_indexed | 2024-03-11T02:17:30Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj.art-89ac17dcedc549198ebb184867e963742023-11-18T11:05:31ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942023-05-017310010.3390/jmmp7030100Determination of the Cutting-Edge Microgeometry Based on Process Forces during Peripheral Milling of Ti-6Al-4V Using Machine LearningMatthias Wimmer0Roman Hartl1Michael F. Zaeh2TUM School of Engineering and Design, Technical University of Munich, 85748 Garching, GermanyTUM School of Engineering and Design, Technical University of Munich, 85748 Garching, GermanyTUM School of Engineering and Design, Technical University of Munich, 85748 Garching, GermanyThe residual stress state of the machined sub-surface influences the service quality indicators of a component, such as fatigue life, tribological properties, and distortion. During machining, the radius of the cutting edge changes due to tool wear. The cutting-edge rounding significantly affects the residual stress state in the part and the occurring process forces. This paper presents a tool wear prediction model based on in-process measured cutting forces. The effects of the cutting-edge geometry on the force behavior and the machining-induced residual stresses were examined experimentally. The resulting database was used to realize a Machine Learning algorithm to calculate the hidden value of tool wear. The predictions were validated by milling experiments using rounded cutting edges for different process parameters. The microgeometry of the cutting edge could be determined with a Root Mean Square Error of 8.94 μm.https://www.mdpi.com/2504-4494/7/3/100millingtitanium alloy Ti-6Al-4Vresidual stressesprocess forcescutting-edge radiusmachine learning |
| spellingShingle | Matthias Wimmer Roman Hartl Michael F. Zaeh Determination of the Cutting-Edge Microgeometry Based on Process Forces during Peripheral Milling of Ti-6Al-4V Using Machine Learning Journal of Manufacturing and Materials Processing milling titanium alloy Ti-6Al-4V residual stresses process forces cutting-edge radius machine learning |
| title | Determination of the Cutting-Edge Microgeometry Based on Process Forces during Peripheral Milling of Ti-6Al-4V Using Machine Learning |
| title_full | Determination of the Cutting-Edge Microgeometry Based on Process Forces during Peripheral Milling of Ti-6Al-4V Using Machine Learning |
| title_fullStr | Determination of the Cutting-Edge Microgeometry Based on Process Forces during Peripheral Milling of Ti-6Al-4V Using Machine Learning |
| title_full_unstemmed | Determination of the Cutting-Edge Microgeometry Based on Process Forces during Peripheral Milling of Ti-6Al-4V Using Machine Learning |
| title_short | Determination of the Cutting-Edge Microgeometry Based on Process Forces during Peripheral Milling of Ti-6Al-4V Using Machine Learning |
| title_sort | determination of the cutting edge microgeometry based on process forces during peripheral milling of ti 6al 4v using machine learning |
| topic | milling titanium alloy Ti-6Al-4V residual stresses process forces cutting-edge radius machine learning |
| url | https://www.mdpi.com/2504-4494/7/3/100 |
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