Optimizing Industrial Etching Processes for PCB Manufacturing : Real-Time Temperature Control Using VGG-Based Transfer Learning
(2024) 2nd International Conference on Intelligent Manufacturing and Robotics (ICiMR) - Taicang, China- Abstract
- Accurate temperature control in Printed Circuit Board (PCB) manufacturing is essential for maintaining high-quality etching results. Automated monitoring us-ing machine vision and deep learning offers an effective approach for this task. This study investigated a feature-based transfer learning technique for classifying temperature readiness in infrared images of the etching process. The captured da-taset containing 470 ‘Production-Ready’ and 480 ‘Not-Ready’ infrared images of the etchant tank was utilized. Pre-trained Visual Geometry Group (VGG) Convo-lutional Neural Network (CNN) models, specifically VGG16 and VGG19, were employed to extract discriminative features from these images. Logistic Regres-sion (LR) classifiers were then... (More)
- Accurate temperature control in Printed Circuit Board (PCB) manufacturing is essential for maintaining high-quality etching results. Automated monitoring us-ing machine vision and deep learning offers an effective approach for this task. This study investigated a feature-based transfer learning technique for classifying temperature readiness in infrared images of the etching process. The captured da-taset containing 470 ‘Production-Ready’ and 480 ‘Not-Ready’ infrared images of the etchant tank was utilized. Pre-trained Visual Geometry Group (VGG) Convo-lutional Neural Network (CNN) models, specifically VGG16 and VGG19, were employed to extract discriminative features from these images. Logistic Regres-sion (LR) classifiers were then trained on these features to classify the infrared images. The performance of the VGG16-LR and VGG19-LR pipelines was evaluated on training, validation, and test sets using a 60:20:20 split. While both pipelines achieved 100% accuracy on the training sets, the VGG19 pipeline showed exceptional performance, achieving a validation accuracy of 95%, and a test accuracy of 99%. The VGG16 pipeline also demonstrated robust perfor-mance, achieving 96% accuracy on both the validation and test sets. Considering the dimensions and the overall efficiency of the pipeline, it was determined that the VGG19-LR model was appropriate for the captured dataset. The high accura-cy indicates that transfer learning is suitable for categorizing temperature fluctua-tion in infrared thermography, as opposed to training a deep neural network from scratch. Computer vision and deep learning provide automated and precise tem-perature management during the etching process, leading to enhanced efficiency in PCB manufacturing. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/fc4956fb-5adb-4063-9486-30ce342a3013
- author
- Luo, Yang ; Jagtap, Sandeep LU ; Trollman, Hana ; Garcia-Garcia, Guillermo ; Liu, Xiaoyan and Majeed, Anwar P.P. Abdul
- organization
- publishing date
- 2024
- type
- Contribution to conference
- publication status
- in press
- subject
- pages
- 10 pages
- conference name
- 2nd International Conference on Intelligent Manufacturing and Robotics (ICiMR) - Taicang, China
- conference location
- Taicang, China
- conference dates
- 2024-08-22 - 2024-08-23
- language
- English
- LU publication?
- yes
- id
- fc4956fb-5adb-4063-9486-30ce342a3013
- date added to LUP
- 2024-09-26 18:03:18
- date last changed
- 2024-09-30 09:51:58
@misc{fc4956fb-5adb-4063-9486-30ce342a3013, abstract = {{Accurate temperature control in Printed Circuit Board (PCB) manufacturing is essential for maintaining high-quality etching results. Automated monitoring us-ing machine vision and deep learning offers an effective approach for this task. This study investigated a feature-based transfer learning technique for classifying temperature readiness in infrared images of the etching process. The captured da-taset containing 470 ‘Production-Ready’ and 480 ‘Not-Ready’ infrared images of the etchant tank was utilized. Pre-trained Visual Geometry Group (VGG) Convo-lutional Neural Network (CNN) models, specifically VGG16 and VGG19, were employed to extract discriminative features from these images. Logistic Regres-sion (LR) classifiers were then trained on these features to classify the infrared images. The performance of the VGG16-LR and VGG19-LR pipelines was evaluated on training, validation, and test sets using a 60:20:20 split. While both pipelines achieved 100% accuracy on the training sets, the VGG19 pipeline showed exceptional performance, achieving a validation accuracy of 95%, and a test accuracy of 99%. The VGG16 pipeline also demonstrated robust perfor-mance, achieving 96% accuracy on both the validation and test sets. Considering the dimensions and the overall efficiency of the pipeline, it was determined that the VGG19-LR model was appropriate for the captured dataset. The high accura-cy indicates that transfer learning is suitable for categorizing temperature fluctua-tion in infrared thermography, as opposed to training a deep neural network from scratch. Computer vision and deep learning provide automated and precise tem-perature management during the etching process, leading to enhanced efficiency in PCB manufacturing.}}, author = {{Luo, Yang and Jagtap, Sandeep and Trollman, Hana and Garcia-Garcia, Guillermo and Liu, Xiaoyan and Majeed, Anwar P.P. Abdul}}, language = {{eng}}, title = {{Optimizing Industrial Etching Processes for PCB Manufacturing : Real-Time Temperature Control Using VGG-Based Transfer Learning}}, year = {{2024}}, }