Optimizing industrial etching processes for PCB manufacturing : real-time temperature control using VGG-based transfer learning
Luo, Yang ; Jagtap, Sandeep LU
; Trollman, Hana
; Garcia-Garcia, Guillermo
; Liu, Xiaoyan
and Majeed, Anwar P.P. Abdul
(2024)
2nd International Conference on Intelligent Manufacturing and Robotics (ICiMR) - Taicang, China
In Lecture Notes in Networks and Systems
1316.
p.353-361
- 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
- 2024-04-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Temperature control, PCB manufacturing, Transfer learning, Infra- red imaging, Feature extraction, Convolutional Neural Networks (CNN)
- host publication
- Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics : ICIMR 2024, 22-23 August, Suzhou, China - ICIMR 2024, 22-23 August, Suzhou, China
- series title
- Lecture Notes in Networks and Systems
- editor
- et al., Wei Chen
- volume
- 1316
- pages
- 9 pages
- publisher
- Springer Nature
- 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
- external identifiers
-
- scopus:105002727662
- ISSN
- 2367-3389
- 2367-3370
- ISBN
- 978-981-96-3948-9
- 978-981-96-3949-6
- DOI
- 10.1007/978-981-96-3949-6_27
- language
- English
- LU publication?
- yes
- id
- fc4956fb-5adb-4063-9486-30ce342a3013
- date added to LUP
- 2024-09-26 18:03:18
- date last changed
- 2025-07-05 17:56:04
@inproceedings{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}},
booktitle = {{Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics : ICIMR 2024, 22-23 August, Suzhou, China}},
editor = {{et al., Wei Chen}},
isbn = {{978-981-96-3948-9}},
issn = {{2367-3389}},
keywords = {{Temperature control; PCB manufacturing; Transfer learning; Infra- red imaging; Feature extraction; Convolutional Neural Networks (CNN)}},
language = {{eng}},
month = {{04}},
pages = {{353--361}},
publisher = {{Springer Nature}},
series = {{Lecture Notes in Networks and Systems}},
title = {{Optimizing industrial etching processes for PCB manufacturing : real-time temperature control using VGG-based transfer learning}},
url = {{http://dx.doi.org/10.1007/978-981-96-3949-6_27}},
doi = {{10.1007/978-981-96-3949-6_27}},
volume = {{1316}},
year = {{2024}},
}