In-Process Tool Condition Forecasting of Drilling CFRP/Ti Stacks Based on ResNet and LSTM Network Article Swipe
Zhenxi Jiang
,
Fuji Wang
,
Debiao Zeng
,
Shaowei Zhu
,
Rao Fu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13031881
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13031881
Tool condition forecasting (TCF) is a key technology for continuous drilling of CFRP/Ti stacks, as the tool wear is always rapid and severe, which may further induce unexpected drilling quality issues. However, for drilling CFRP/Ti stacks, the cutting spindle power and vibration signals change are complex, influenced by many factors due to the different materials properties. The TCF for drilling CFRP/Ti stacks remains challenging, as the sensitive features are difficult to extract, which decide the accuracy and robustness. Aiming to monitor and forecast tool wear of drilling CFRP/Ti stacks, an in-process TCF method based on residual neural network (ResNet) and long short-term memory (LSTM) network has been proposed in this paper. Using the cutting spindle power and vibration signals preprocessed by the proposed method, the LSTM network with the ResNet-based model integrated can forecast tool-wear values of the next drilling holes. A case study demonstrated the effectiveness of TCF, where the results using raw measured signals and preprocessed datasets are tested for comparison. The mean absolute error (MAE) using raw signals is 45.01 μm, which is 2.20 times bigger than that using preprocess signals. With the proposed method, the data preprocessing for drilling CFRP/Ti stacks can improve the tool-wear forecasting accuracy to MAE 20.43μm level, which meets the demand for online TCF.
Related Topics
Concepts
Drilling
Preprocessor
Tool wear
Robustness (evolution)
Long short term memory
Artificial neural network
Computer science
Power (physics)
Materials science
Artificial intelligence
Mechanical engineering
Engineering
Machining
Recurrent neural network
Quantum mechanics
Chemistry
Biochemistry
Gene
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app13031881
- https://www.mdpi.com/2076-3417/13/3/1881/pdf?version=1675221867
- OA Status
- gold
- Cited By
- 6
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4318830480
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4318830480Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app13031881Digital Object Identifier
- Title
-
In-Process Tool Condition Forecasting of Drilling CFRP/Ti Stacks Based on ResNet and LSTM NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-01Full publication date if available
- Authors
-
Zhenxi Jiang, Fuji Wang, Debiao Zeng, Shaowei Zhu, Rao FuList of authors in order
- Landing page
-
https://doi.org/10.3390/app13031881Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/13/3/1881/pdf?version=1675221867Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/13/3/1881/pdf?version=1675221867Direct OA link when available
- Concepts
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Drilling, Preprocessor, Tool wear, Robustness (evolution), Long short term memory, Artificial neural network, Computer science, Power (physics), Materials science, Artificial intelligence, Mechanical engineering, Engineering, Machining, Recurrent neural network, Quantum mechanics, Chemistry, Biochemistry, Gene, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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