Enhancing Printed Circuit Board Defect Detection through Ensemble Learning Article Swipe
K.F. Law
,
Minhao Yu
,
Liangrui Zhang
,
Yiyi Zhang
,
Peng Xu
,
Jerry Gao
,
Jun Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/fityr63263.2024.00013
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/fityr63263.2024.00013
The quality control of printed circuit boards (PCBs) is paramount in\nadvancing electronic device technology. While numerous machine learning\nmethodologies have been utilized to augment defect detection efficiency and\naccuracy, previous studies have predominantly focused on optimizing individual\nmodels for specific defect types, often overlooking the potential synergies\nbetween different approaches. This paper introduces a comprehensive inspection\nframework leveraging an ensemble learning strategy to address this gap.\nInitially, we utilize four distinct PCB defect detection models utilizing\nstate-of-the-art methods: EfficientDet, MobileNet SSDv2, Faster RCNN, and\nYOLOv5. Each method is capable of identifying PCB defects independently.\nSubsequently, we integrate these models into an ensemble learning framework to\nenhance detection performance. A comparative analysis reveals that our ensemble\nlearning framework significantly outperforms individual methods, achieving a\n95% accuracy in detecting diverse PCB defects. These findings underscore the\nefficacy of our proposed ensemble learning framework in enhancing PCB quality\ncontrol processes.\n
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- Type
- preprint
- Language
- en
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- https://doi.org/10.1109/fityr63263.2024.00013
- OA Status
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All OpenAlex metadata
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https://openalex.org/W4403667664Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/fityr63263.2024.00013Digital Object Identifier
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Enhancing Printed Circuit Board Defect Detection through Ensemble LearningWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-15Full publication date if available
- Authors
-
K.F. Law, Minhao Yu, Liangrui Zhang, Yiyi Zhang, Peng Xu, Jerry Gao, Jun LiuList of authors in order
- Landing page
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https://doi.org/10.1109/fityr63263.2024.00013Publisher landing page
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2409.09555Direct OA link when available
- Concepts
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Ensemble learning, Computer science, Printed circuit board, Machine learning, Artificial intelligence, Deep learning, Quality (philosophy), Control (management), Epistemology, Operating system, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.device | 12 |
| abstract_inverted_index.method | 78 |
| abstract_inverted_index.models | 68, 89 |
| abstract_inverted_index.types, | 38 |
| abstract_inverted_index.address | 58 |
| abstract_inverted_index.augment | 22 |
| abstract_inverted_index.capable | 80 |
| abstract_inverted_index.circuit | 5 |
| abstract_inverted_index.control | 2 |
| abstract_inverted_index.defects | 84 |
| abstract_inverted_index.diverse | 115 |
| abstract_inverted_index.focused | 31 |
| abstract_inverted_index.machine | 16 |
| abstract_inverted_index.printed | 4 |
| abstract_inverted_index.quality | 1 |
| abstract_inverted_index.reveals | 101 |
| abstract_inverted_index.studies | 28 |
| abstract_inverted_index.utilize | 62 |
| abstract_inverted_index.accuracy | 112 |
| abstract_inverted_index.analysis | 100 |
| abstract_inverted_index.defects. | 117 |
| abstract_inverted_index.distinct | 64 |
| abstract_inverted_index.ensemble | 54, 92, 125 |
| abstract_inverted_index.findings | 119 |
| abstract_inverted_index.learning | 55, 93, 126 |
| abstract_inverted_index.methods, | 109 |
| abstract_inverted_index.methods: | 70 |
| abstract_inverted_index.numerous | 15 |
| abstract_inverted_index.previous | 27 |
| abstract_inverted_index.proposed | 124 |
| abstract_inverted_index.specific | 36 |
| abstract_inverted_index.strategy | 56 |
| abstract_inverted_index.utilized | 20 |
| abstract_inverted_index.MobileNet | 72 |
| abstract_inverted_index.achieving | 110 |
| abstract_inverted_index.detecting | 114 |
| abstract_inverted_index.detection | 24, 67, 96 |
| abstract_inverted_index.different | 44 |
| abstract_inverted_index.enhancing | 129 |
| abstract_inverted_index.framework | 94, 105, 127 |
| abstract_inverted_index.integrate | 87 |
| abstract_inverted_index.paramount | 9 |
| abstract_inverted_index.potential | 42 |
| abstract_inverted_index.efficiency | 25 |
| abstract_inverted_index.electronic | 11 |
| abstract_inverted_index.individual | 108 |
| abstract_inverted_index.introduces | 48 |
| abstract_inverted_index.leveraging | 52 |
| abstract_inverted_index.optimizing | 33 |
| abstract_inverted_index.underscore | 120 |
| abstract_inverted_index.approaches. | 45 |
| abstract_inverted_index.comparative | 99 |
| abstract_inverted_index.identifying | 82 |
| abstract_inverted_index.outperforms | 107 |
| abstract_inverted_index.overlooking | 40 |
| abstract_inverted_index.technology. | 13 |
| abstract_inverted_index.to\nenhance | 95 |
| abstract_inverted_index.and\nYOLOv5. | 76 |
| abstract_inverted_index.performance. | 97 |
| abstract_inverted_index.processes.\n | 132 |
| abstract_inverted_index.EfficientDet, | 71 |
| abstract_inverted_index.comprehensive | 50 |
| abstract_inverted_index.in\nadvancing | 10 |
| abstract_inverted_index.predominantly | 30 |
| abstract_inverted_index.significantly | 106 |
| abstract_inverted_index.the\nefficacy | 121 |
| abstract_inverted_index.and\naccuracy, | 26 |
| abstract_inverted_index.gap.\nInitially, | 60 |
| abstract_inverted_index.quality\ncontrol | 131 |
| abstract_inverted_index.ensemble\nlearning | 104 |
| abstract_inverted_index.individual\nmodels | 34 |
| abstract_inverted_index.synergies\nbetween | 43 |
| abstract_inverted_index.inspection\nframework | 51 |
| abstract_inverted_index.learning\nmethodologies | 17 |
| abstract_inverted_index.utilizing\nstate-of-the-art | 69 |
| abstract_inverted_index.independently.\nSubsequently, | 85 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.87699311 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |