Active learning for the prediction of shape errors in milling

authored by

Berend Denkena, Marcel Wichmanna, Markus Rokickib, Lukas Stürenburg

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.

Organisation(s)

Institute of Production Engineering and Machine Tools
L3S Research Centre

Type

Conference article

Journal

Procedia CIRP

Volume

126

Pages

324-329

No. of pages

6

ISSN

2212-8271

Publication date

2024

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Control and Systems Engineering, Industrial and Manufacturing Engineering

Electronic version(s)

https://doi.org/10.1016/j.procir.2024.08.364 (Access: Open)