Details
| Original language | English |
|---|---|
| Pages (from-to) | 324-329 |
| Number of pages | 6 |
| Journal | Procedia CIRP |
| Volume | 126 |
| Early online date | 9 Oct 2024 |
| Publication status | Published - 2024 |
| Event | 17th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2023 - Naples, Italy Duration: 12 Jul 2023 → 14 Jul 2023 |
Abstract
In machining processes, various influences, such as workpiece and tool geometry, process parameters or tool wear, can lead to decreasing part quality. Thus, predicting these influences to enable better process planning is essential. However, no general, analytical model is available to facilitate this. Data-driven approaches, on the other hand, are costly due to the required data labeling efforts. To address this, the authors present a data-driven active machine learning approach to predict shape errors based on process data enhanced by a material removal simulation. Using two representative pocket milling datasets, it is shown that this approach can yield better (up to 5 % decrease of RMSE) and more consistent (up to 85 % decrease in standard deviation of RMSE) model accuracy for a given budget of tactile probing points compared to a passive learning strategy.
Keywords
- Adaptive manufacutring, Machine learning, Machining, Milling, Predictive model
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 126, 2024, p. 324-329.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Active learning for the prediction of shape errors in milling
AU - Denkena, Berend
AU - Wichmanna, Marcel
AU - Rokickib, Markus
AU - Stürenburg, Lukas
N1 - Publisher Copyright: © 2024 Elsevier B.V.. All rights reserved.
PY - 2024
Y1 - 2024
N2 - In machining processes, various influences, such as workpiece and tool geometry, process parameters or tool wear, can lead to decreasing part quality. Thus, predicting these influences to enable better process planning is essential. However, no general, analytical model is available to facilitate this. Data-driven approaches, on the other hand, are costly due to the required data labeling efforts. To address this, the authors present a data-driven active machine learning approach to predict shape errors based on process data enhanced by a material removal simulation. Using two representative pocket milling datasets, it is shown that this approach can yield better (up to 5 % decrease of RMSE) and more consistent (up to 85 % decrease in standard deviation of RMSE) model accuracy for a given budget of tactile probing points compared to a passive learning strategy.
AB - In machining processes, various influences, such as workpiece and tool geometry, process parameters or tool wear, can lead to decreasing part quality. Thus, predicting these influences to enable better process planning is essential. However, no general, analytical model is available to facilitate this. Data-driven approaches, on the other hand, are costly due to the required data labeling efforts. To address this, the authors present a data-driven active machine learning approach to predict shape errors based on process data enhanced by a material removal simulation. Using two representative pocket milling datasets, it is shown that this approach can yield better (up to 5 % decrease of RMSE) and more consistent (up to 85 % decrease in standard deviation of RMSE) model accuracy for a given budget of tactile probing points compared to a passive learning strategy.
KW - Adaptive manufacutring
KW - Machine learning
KW - Machining
KW - Milling
KW - Predictive model
UR - http://www.scopus.com/inward/record.url?scp=85208553564&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2024.08.364
DO - 10.1016/j.procir.2024.08.364
M3 - Conference article
AN - SCOPUS:85208553564
VL - 126
SP - 324
EP - 329
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 17th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2023
Y2 - 12 July 2023 through 14 July 2023
ER -