Optimizing industrial etching processes for PCB manufacturing: real-time temperature control using VGG-based transfer learning

Date published

2025-04-01

Free to read from

2025-04-16

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

Springer

Department

Type

Conference paper

ISSN

Format

Citation

Luo Y, Jagtap S, Trollman H, et al., (2025) Optimizing industrial etching processes for PCB manufacturing: real-time temperature control using VGG-based transfer learning. In: Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics (ICIMR 2024) 22-23 August, Suzhou, China. Lecture Notes in Networks and Systems, Volume 1316, Springer Nature Singapore, 2025, pp. 353-361

Abstract

Accurate temperature control in Printed Circuit Board (PCB) manufacturing is essential for maintaining high-quality etching results. Automated monitoring using 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 dataset containing 470 ‘Production-Ready’ and 480 ‘Not-Ready’ infrared images of the etchant tank was utilized. Pre-trained Visual Geometry Group (VGG) Convolutional Neural Network (CNN) models, specifically VGG16 and VGG19, were employed to extract discriminative features from these images. Logistic Regression (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 performance, 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 accuracy indicates that transfer learning is suitable for categorizing temperature fluctuation in infrared thermography, as opposed to training a deep neural network from scratch. Computer vision and deep learning provide automated and precise temperature management during the etching process, leading to enhanced efficiency in PCB manufacturing.

Description

Software Description

Software Language

Github

Keywords

46 Information and Computing Sciences, 4014 Manufacturing Engineering, 40 Engineering, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD)

DOI

Rights

Attribution 4.0 International

Relationships

Relationships

Resources

Funder/s

European Commission
This research was funded by Research Development Fund, Grant Num-ber: RDF-21-01-028; Summer Undergraduate Research Fellowship, Grant Number: SURF-2024-0355; and Project for Centre of Excellence for Syntegrative Education, Grant Number: COESE2324-01-07 of Xi’an Jiaotong-Liverpool University. Guillermo Garcia-Garcia acknowledges the Grant ‘Marie Skłodowska-Curie Actions (MSCA) Postdoctoral Fellowship’ with Grant agreement ID: 101052284