A Flatness Error Prediction Model in Face Milling Operations Using 6-DOF Robotic Arms Article Swipe
I. Iglesias
,
Alberto Sánchez Lite
,
Cristina González-Gaya
,
F.J.G. Silva
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jmmp9020066
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jmmp9020066
The current trend in machining with robotic arms involves leveraging Industry 4.0 technologies to propose solutions that reduce path deviation errors. This approach presents significant challenges alongside promising advancements, as well as a substantial increase in the cost of future industrial robotic cells, which is not always amortizable. As an alternative or complementary approach to this trend, methods encouraging the occasional use of Industry 4.0 devices for characterizing the behavior of the actual physical cell, calibration, or adjustment are proposed. One such method, called FlePFaM, predicts flatness errors in face milling operations using robotic arms. This is achieved by estimating tool path deviation errors through the integration of a simple model of the robot arm’s mechanics with the cutting forces vector of the process, thereby optimizing machining conditions. These conditions are determined through prior empirical estimations of mass, stiffness, and damping. The conducted tests enabled the selection of the most favorable combination of variables, such as the robot wrist configuration, the position and orientation of the workpiece, and the predominant milling orientation. This led to the identification of the configuration with the lowest absolute flatness error according to the model’s predictions. The results demonstrated a high degree of similarity—between 97% for the closest case and 57% for the farthest case—between simulated and experimental flatness error values. FlePFaM represents a significant step forward in adopting innovative robotic arm solutions for reliable and efficient production. FlePFaM includes dimensional flatness indicators that provide practical support for decision making.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jmmp9020066
- OA Status
- gold
- Cited By
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4407751351Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/jmmp9020066Digital Object Identifier
- Title
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A Flatness Error Prediction Model in Face Milling Operations Using 6-DOF Robotic ArmsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-19Full publication date if available
- Authors
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I. Iglesias, Alberto Sánchez Lite, Cristina González-Gaya, F.J.G. SilvaList of authors in order
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https://doi.org/10.3390/jmmp9020066Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/jmmp9020066Direct OA link when available
- Concepts
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Flatness (cosmology), Face (sociological concept), Computer science, Artificial intelligence, Computer vision, Engineering, Physics, Philosophy, Quantum mechanics, Linguistics, CosmologyTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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55Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2025-02-19 |
| publication_year | 2025 |
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| referenced_works_count | 55 |
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