Incorporating process quality capability into process parameter tuning for high-voltage power cable manufacturing.
Saved in:
| Title: | Incorporating process quality capability into process parameter tuning for high-voltage power cable manufacturing. |
|---|---|
| Authors: | Yang, Chao-Lung1 (AUTHOR) clyang@mail.ntust.edu.tw, Chu, Tzu-Hsien1 (AUTHOR), Chen, Guan-Ying1 (AUTHOR), Huang, Chae-Wu2 (AUTHOR) |
| Source: | International Journal of Advanced Manufacturing Technology. Sep2026, Vol. 146 Issue 3, p1671-1685. 15p. |
| Subjects: | Process optimization, Process capability, Reinforcement learning, Computational intelligence, Generative artificial intelligence, Manufacturing industries |
| Abstract: | High-voltage power cables manufacturing can be considered continual production system. Optimizing process parameters is significant for continual mass production, especially for large-scale processes. A decision-making agent based on computational intelligence was used to optimize process parameters using signals from phasor measurement units in dynamic processes. This study proposed an innovative framework for optimizing process parameters, specifically for the manufacturing of high-voltage power cables under large-scale processes. A unified benchmark was used to access the optimization decisions of data-driven decision-making agents using the Variational Autoencoder (VAE) model and a Deterministic Policy Gradient (DPG) model. Cpk, a common process capability metric, was used as an example to evaluate the optimization performance of the VAE and DPG models, and real data from high-voltage cable manufacturing was used to validate the proposed framework. The results showed that the DPG model outperformed the VAE model when the optimal control strategy was unknown. Compared to the existing method, the parameters optimized by the DPG model led to improvements of 24% and 36% in Cpk for a single production line and a composite production line, respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
