Developing a deep learning-based uncertainty-aware tool wear prediction method using smartphone sensors for the turning process of Ti-6Al-4V

Kim, Gyeongho; Yang, Sang Min; Kim, Dong Min; Choi, Jae Gyeong; Lim, Sunghoon; Park, Hyung Wook

doi:10.1016/j.jmsy.2024.07.010

Journal of Manufacturing Systems

2024

DOI: 10.1016/j.jmsy.2024.07.010

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Developing a deep learning-based uncertainty-aware tool wear prediction method using smartphone sensors for the turning process of Ti-6Al-4V

Gyeongho Kim,

Sang Min Yang,

Dong Min Kim

et al.

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Cited by 3 publications

References 70 publications

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Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach

Thomas,

Kuang,

Wang

et al. 2024

Vis Comput

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The implementation of Smart Airport and Airport 4.0 visions relies on the integration of automation, artificial intelligence, data science, and aviation technology to enhance passenger experiences and operational efficiency. One essential factor in the integration is the semantic segmentation of the aircraft main components (AMC) perception, which is essential to maintenance, repair, and operations in aircraft and airport operations. However, AMC segmentation has challenges from low data availability, high-quality annotation scarcity, and categorical imbalance, which are common in practical applications, including aviation. This study proposes a novel AMC segmentation solution, employing a transfer learning framework based on a sophisticated DeepLabV3 architecture optimized with a custom-designed Focal Dice Loss function. The proposed solution remarkably suppresses the categorical imbalance challenge and increases the dataset variability with manually annotated images and dynamic augmentation strategies to train a robust AMC segmentation model. The model achieved a notable intersection over union of 84.002% and an accuracy of 91.466%, significantly advancing the AMC segmentation performance. These results demonstrate the effectiveness of the proposed AMC segmentation solution in aircraft and airport operation scenarios. This study provides a pioneering solution to the AMC semantic perception problem and contributes a valuable dataset to the community, which is fundamental to future research on aircraft and airport semantic perception. Graphical abstract

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Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach

Thomas,

Kuang,

Wang

et al. 2024

Vis Comput

View full text Add to dashboard Cite

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Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions

Kim,

Kang,

Yang

et al. 2025

Reliability Engineering & System Safety

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RBNN with cycle-based parameter updates and its application to RUL prediction

Guo,

Wang,

Luo

2024

Meas. Sci. Technol.

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Bayesian neural networks (BNNs) combine Bayesian theory with deep learning, providing a probabilistic interpretation of deep learning models. Traditional BNNs typically assume that network’s parameters follow a standard Gaussian distribution, which may not accurately reflect the actual situation. To address this issue, this paper proposes a Recurrent Bayesian Neural Network (RBNN) that employs two trainable Gaussian distributions as priors. Furthermore, an alternating cycle model parameter updation algorithm is designed accordingly, which not only prevents overfitting to specific data during training but also enhances the robustness of model parameters in error approximation. Additionally, this RBNN module can be integrated with classical neural network architecture conveniently. By integrating the RBNN with a temporal convolutional network (TCN), the effectiveness of the proposed approach was validated using a dataset for remaining useful life (RUL) prediction of gear and bearing components in civil aircraft. Comparative studies show that the integrated model outperforms traditional methods in terms of RUL prediction and uncertainty quantification, offering superior accuracy and robustness, and the RMSE of the RBNN prediction results is reduced by 17.7% and score is increased by 27.5% compared to BNN.

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Developing a deep learning-based uncertainty-aware tool wear prediction method using smartphone sensors for the turning process of Ti-6Al-4V

Cited by 3 publications

References 70 publications

Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach

Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach

Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions

RBNN with cycle-based parameter updates and its application to RUL prediction

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