Optimizing Industrial Etching Processes for PCB Manufacturing: Real-Time Temperature Control Using VGG-Based Transfer Learning

Yang Luo, Sandeep Jagtap, Hana Trollman, Guillermo Garcia-Garcia, Xiaoyan Liu, Anwar P.P. Abdul Majeed*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

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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.
Original languageEnglish
Title of host publicationInternational Conference on Intelligent Manufacturing and Robotics 2024
Subtitle of host publicationICiMR 2024
PublisherSpringer
Publication statusAccepted/In press - 2024
Event2nd International Conference on Intelligent Manufacturing and Robotics (ICiMR) - Taicang, China
Duration: 22 Aug 202423 Aug 2024

Conference

Conference2nd International Conference on Intelligent Manufacturing and Robotics (ICiMR)
Country/TerritoryChina
Period22/08/2423/08/24

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