Tool wear prediction in milling CFRP with different fiber orientations based on multi-channel 1DCNN-LSTM

Bohao Li, Zhenghui Lu, Xiaoliang Jin () and Liping Zhao
Additional contact information
Bohao Li: Xi’an Jiaotong University
Zhenghui Lu: The University of British Columbia
Xiaoliang Jin: The University of British Columbia
Liping Zhao: Xi’an Jiaotong University

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 6, No 5, 2547-2566

Abstract: Abstract In the machining of carbon fiber reinforced polymer (CFRP) components, tool wear grows rapidly due to the highly abrasive property of carbon fibers, resulting in unfavorable part quality such as delamination and fiber pullout. The tool wear progression, cutting forces, and their quantitative relationship are highly dependent on the fiber orientation. This paper predicts the tool wear progression based on cutting force signals in milling unidirectional (UD) CFRP with the fiber orientation effect. A deep learning model based on multi-channel 1D convolutional neural network (CNN) and long short-term memory (LSTM) is used, with the benefit of considering the unique force variation features due to the CFRP anisotropy. Milling experiments with the fiber orientations of 0°, 45°, 90°, and 135° under different feed rates and cutting speeds were performed. The multi-channel 1D CNN obtains the force signal features from individual univariate time series, and combines the time-series information from all channels as the final feature representation in the final layer of the network. The LSTM layer extracts the temporal and spatial characteristics of the dynamic force signals. Moving window and distribution analysis techniques are applied to the predicted tool wear results to reduce the effect of force signal disturbance caused by CFRP inhomogeneity. The model achieves the tool wear prediction with R2 of 95.04% and MAE of 2.94 μm. Moreover, benefiting from its high feature representation capacity, the proposed method shows higher prediction accuracy for different cutting conditions with more than 25% improvement compared to other commonly used data-driven methods, including 1DCNN, 2DCNN, LSTM, BPNN, and SVR.

Keywords: Tool wear; Carbon fiber reinforced polymer; Cutting force; Machine learning; Fiber orientation (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02164-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:35:y:2024:i:6:d:10.1007_s10845-023-02164-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02164-7

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().