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Article

Innovative Anomaly Detection in PCB Hot-Air Ovens Using Adaptive Temporal Feature Mapping

1
Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan
2
Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan
3
Department of Industrial Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(19), 10771; https://doi.org/10.3390/app151910771
Submission received: 25 June 2025 / Revised: 5 September 2025 / Accepted: 10 September 2025 / Published: 7 October 2025

Abstract

As automated equipment in PCB manufacturing becomes increasingly reliant on precision hot-air ovens, ensuring operational stability and reducing downtime have become critical challenges. Existing anomaly detection methods, such as Support Vector Machines (SVMs), Deep Neural Networks (DNNs), and Long Short-Term Memory (LSTM) Networks, struggle with high-dimensional dynamic data, leading to inefficiencies and overfitting. To address these issues, this study proposes an innovative anomaly detection system specifically designed for fault diagnosis in PCB hot-air ovens. The motivation is to improve accuracy and efficiency while adapting to dynamic changes in the manufacturing environment. The core innovation lies in the introduction of the Adaptive Temporal Feature Map (ATFM), which dynamically extracts and adjusts key temporal features in real time. By combining ATFM with Bi-Directional Dimensionality Reduction (BDDR) and eXtreme Gradient Boosting (XGBoost), the system effectively handles high-dimensional data and adapts its parameters based on evolving data patterns, significantly enhancing fault detection accuracy and efficiency. The experimental results show a fault prediction accuracy of 99.33%, greatly reducing machine downtime and product defects compared to traditional methods.
Keywords: adaptive temporal feature map; anomaly detection; XGBoost; fault diagnosis; PCB manufacturing adaptive temporal feature map; anomaly detection; XGBoost; fault diagnosis; PCB manufacturing

Share and Cite

MDPI and ACS Style

Cheng, C.-Y.; Chien, C.-M.; Chen, T.-L.; Yuangyai, C.; Kong, P.-l. Innovative Anomaly Detection in PCB Hot-Air Ovens Using Adaptive Temporal Feature Mapping. Appl. Sci. 2025, 15, 10771. https://doi.org/10.3390/app151910771

AMA Style

Cheng C-Y, Chien C-M, Chen T-L, Yuangyai C, Kong P-l. Innovative Anomaly Detection in PCB Hot-Air Ovens Using Adaptive Temporal Feature Mapping. Applied Sciences. 2025; 15(19):10771. https://doi.org/10.3390/app151910771

Chicago/Turabian Style

Cheng, Chen-Yang, Chuan-Min Chien, Tzu-Li Chen, Chumpol Yuangyai, and Pei-ling Kong. 2025. "Innovative Anomaly Detection in PCB Hot-Air Ovens Using Adaptive Temporal Feature Mapping" Applied Sciences 15, no. 19: 10771. https://doi.org/10.3390/app151910771

APA Style

Cheng, C.-Y., Chien, C.-M., Chen, T.-L., Yuangyai, C., & Kong, P.-l. (2025). Innovative Anomaly Detection in PCB Hot-Air Ovens Using Adaptive Temporal Feature Mapping. Applied Sciences, 15(19), 10771. https://doi.org/10.3390/app151910771

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