Explainable AI Methods for Identification of Glue Volume Deficiencies in Printed Circuit Boards Article Swipe
Theodoros Tziolas
,
K. Papageorgiou
,
T.C. Theodosiou
,
Dimosthenis Ioannidis
,
Nikolaos Dimitriou
,
Gregory Tinker
,
Elpiniki I. Papageorgiou
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15169061
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15169061
In printed circuit board (PCB) assembly, the volume of dispensed glue is closely related to the PCB’s durability, production costs, and the overall product reliability. Currently, quality inspection is performed manually by operators, inheriting the limitations of human-performed procedures. To address this, we propose an automatic optical inspection framework that utilizes convolutional neural networks (CNNs) and post-hoc explainable methods. Our methodology handles glue quality inspection as a three-fold procedure. Initially, a detection system based on CenterNet MobileNetV2 is developed to localize PCBs, thus, offering a flexible lightweight tool for targeting and cropping regions of interest. Consequently, a CNN is proposed to classify PCB images into three classes based on the placed glue volume achieving 92.2% accuracy. This classification step ensures that varying glue volumes are accurately assessed, addressing potential quality issues that appear early in the production process. Finally, the Deep SHAP and Grad-CAM methods are applied to the CNN classifier to produce explanations of the decision making and further increase the interpretability of the proposed approach, targeting human-centered artificial intelligence. These post-hoc explainable methods provide visual explanations of the model’s decision-making process, offering insights into which features and regions contribute to each classification decision. The proposed method is validated with real industrial data, demonstrating its practical applicability and robustness. The evaluation procedure indicates that the proposed framework offers increased accuracy, low latency, and high-quality visual explanations, thereby strengthening quality assurance in PCB manufacturing.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app15169061
- https://www.mdpi.com/2076-3417/15/16/9061/pdf?version=1755437332
- OA Status
- gold
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413279110
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4413279110Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app15169061Digital Object Identifier
- Title
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Explainable AI Methods for Identification of Glue Volume Deficiencies in Printed Circuit BoardsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-17Full publication date if available
- Authors
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Theodoros Tziolas, K. Papageorgiou, T.C. Theodosiou, Dimosthenis Ioannidis, Nikolaos Dimitriou, Gregory Tinker, Elpiniki I. PapageorgiouList of authors in order
- Landing page
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https://doi.org/10.3390/app15169061Publisher landing page
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https://www.mdpi.com/2076-3417/15/16/9061/pdf?version=1755437332Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/15/16/9061/pdf?version=1755437332Direct OA link when available
- Concepts
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GLUE, Printed circuit board, Identification (biology), Computer science, Engineering drawing, Engineering, Mechanical engineering, Electrical engineering, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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61Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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