ArticleScientific reports2025
Deep learning based gasket fault detection: a CNN approach.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects.Biosensors · 2026Pooled it
- Article
- A Time-Frequency Domain Diagnosis Network for ICE Fault Detection.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gasket inspection is a critical step in the quality control of a product. The proposed method automates the detection of misaligned or incorrectly fitting gaskets, ensuring timely repair action. The suggested method uses deep learning approaches to recognize and evaluate radiator images, with a focus on identifying misaligned or incorrectly installed gaskets. Deep learning algorithms are specific for feature extraction and classification together with a convolutional neural network (CNN) module that allows for seamless connection. A gasket inspection system based on a CNN architecture is developed in this work. The system consists of two sets of convolution layers, followed by two sets of batch normalization layer, two sets of RELU layer, max pooling layer and finally fully connected layer for classification of gasket images. The obtained results indicate that our system has great potential for practical applications in the manufacturing industry. Moreover, our system provides a reliable and efficient mechanism for quality control, which can help reduce the risk of defects and ensure product reliability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.