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This work investigates the use of AI and deep learning for quality control of inkjet-printed electrodes.

December 20, 2025Deleted JournalOpen Access

Computer vision and deep learning-based prediction for inkjet-printed electrodes

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Computer vision methods improve defect detection and classification of inkjet-printed electrodes, suggesting enhanced quality control.

This work investigates the use of AI and deep learning for quality control of inkjet-printed electrodes.
Applied computer vision and deep learning techniques for quality classification.
Utilized Convolutional Neural Networks and Feedforward Neural Networks for defect detection.
Implemented Neural Architecture Search for automated model design.
Achieved a testing accuracy of 90.9% for the classification task.
Obtained a precision of 88.9% on a dataset of 2,406 electrode images.
Demonstrated that CNN models are effective for image classification of printed electrodes.

Quinn et al. (2025) studied this question.

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December 20, 2025Deleted JournalOpen Access

View Full Paper

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Authors

Computer vision methods improve defect detection and classification of inkjet-printed electrodes, suggesting enhanced quality control.

This work investigates the use of AI and deep learning for quality control of inkjet-printed electrodes.
Applied computer vision and deep learning techniques for quality classification.
Utilized Convolutional Neural Networks and Feedforward Neural Networks for defect detection.
Implemented Neural Architecture Search for automated model design.
Achieved a testing accuracy of 90.9% for the classification task.
Obtained a precision of 88.9% on a dataset of 2,406 electrode images.
Demonstrated that CNN models are effective for image classification of printed electrodes.

Quinn et al. (2025) studied this question.

View Full Paper

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