Surrogate-based optimization framework for enhancing SMT process quality and productivity in electronics manufacturing services.
Saved in:
| Title: | Surrogate-based optimization framework for enhancing SMT process quality and productivity in electronics manufacturing services. |
|---|---|
| Authors: | Lee, Ching-Hung1,2 (AUTHOR), Lin, Kai-Xiang1 (AUTHOR), Chou, Che-Wei3 (AUTHOR) cwchou@fcu.edu.tw |
| Source: | International Journal of Advanced Manufacturing Technology. Feb2025, Vol. 136 Issue 11, p5103-5122. 20p. |
| Subjects: | Surface mount technology, Solder joints, Surrogate-based optimization, Optimization algorithms, Particle swarm optimization, Solder pastes, Solder & soldering, Deep learning |
| Abstract: | The quality of the surface mount technology (SMT) process directly impacts product efficiency and reliability. Solder paste printing and reflow soldering processes are vital for assembling high-quality electronic components. Effectively optimizing these process parameters to ensure product consistency and reliability has become a critical issue in the electronics manufacturing services industry. Motivated by realistic needs to enhance the quality of the SMT process, this study integrates a surrogate-based optimization framework combining neural networks and particle swarm optimization (PSO) techniques to minimize experiment counts in SMT processes. Furthermore, a defect detection system utilizing YOLOv4 achieves real-time solder joint quality classification, significantly reducing production downtime and costs. This study proposes a surrogate-based optimization framework to improve the quality and productivity of the SMT production line. It encompasses five stages: domain knowledge, design of experiment, data collection and analysis, modeling, and optimization. Statistical correlation analysis and experimental design are used to reduce experiment counts. Then, neural networks and optimization algorithms are utilized to identify the optimal process parameters in the solder paste printing process. Moreover, this study proposes transfer learning methods for cross-product and line parameter optimization, which not only reduces production changeover time but also offers valuable insights for developing the solder paste printing process. A heat transfer model derived from a single experiment is used to identify parameters for reflow soldering. The target function is then optimized to find the optimal reflow recipe. Additionally, a solder joint defect detection system is established using deep learning and image processing techniques, capable of real-time detection and classification of solder joint defects. To evaluate the validity of the proposed framework, the surrogate-based optimization framework was deployed in a leading networking solutions company in Taiwan. Indeed, the developed solution has been implemented in this case company. [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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | The quality of the surface mount technology (SMT) process directly impacts product efficiency and reliability. Solder paste printing and reflow soldering processes are vital for assembling high-quality electronic components. Effectively optimizing these process parameters to ensure product consistency and reliability has become a critical issue in the electronics manufacturing services industry. Motivated by realistic needs to enhance the quality of the SMT process, this study integrates a surrogate-based optimization framework combining neural networks and particle swarm optimization (PSO) techniques to minimize experiment counts in SMT processes. Furthermore, a defect detection system utilizing YOLOv4 achieves real-time solder joint quality classification, significantly reducing production downtime and costs. This study proposes a surrogate-based optimization framework to improve the quality and productivity of the SMT production line. It encompasses five stages: domain knowledge, design of experiment, data collection and analysis, modeling, and optimization. Statistical correlation analysis and experimental design are used to reduce experiment counts. Then, neural networks and optimization algorithms are utilized to identify the optimal process parameters in the solder paste printing process. Moreover, this study proposes transfer learning methods for cross-product and line parameter optimization, which not only reduces production changeover time but also offers valuable insights for developing the solder paste printing process. A heat transfer model derived from a single experiment is used to identify parameters for reflow soldering. The target function is then optimized to find the optimal reflow recipe. Additionally, a solder joint defect detection system is established using deep learning and image processing techniques, capable of real-time detection and classification of solder joint defects. To evaluate the validity of the proposed framework, the surrogate-based optimization framework was deployed in a leading networking solutions company in Taiwan. Indeed, the developed solution has been implemented in this case company. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 02683768 |
| DOI: | 10.1007/s00170-025-15032-w |
