Defect Detection for Enhanced Traceability in Naval Construction
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an intelligent defect detection method for small shipbuilding components using unsupervised learning and convolutional autoencoders (CAEs). The approach enhances quality control in naval construction by automating the identification of defects in pre-assemblies.
Area Of Science
- Naval Architecture and Marine Engineering
- Digital Manufacturing
- Artificial Intelligence in Quality Control
Background
- Digitalization is transforming shipbuilding, emphasizing traceability for quality assurance and efficiency.
- Defect traceability is crucial for identifying and resolving issues in complex naval construction.
- Minor pre-assemblies are foundational, yet defects here can escalate if undetected.
Purpose Of The Study
- To propose an intelligent, automated method for detecting defects in minor pre-assembly components within shipbuilding.
- To implement unsupervised learning using convolutional autoencoders (CAEs) for defect identification.
- To evaluate the efficacy of various CAE models in detecting overshooting defects.
Main Methods
- Utilized unsupervised learning with Convolutional Autoencoders (CAEs) for automated defect detection.
- Focused on identifying overshooting defects in small, simple pre-assembly pieces.
- Evaluated five distinct CAE architectures: BaseLineCAE, InceptionCAE, SkipCAE, ResNetCAE, and MVTecCAE.
Main Results
- Demonstrated the feasibility of using CAEs for automated defect detection in shipbuilding pre-assemblies.
- Provided a comparative analysis of different CAE models for this specific application.
- Highlighted the potential for scalable and efficient quality control through AI.
Conclusions
- Unsupervised learning with CAEs offers a promising solution for defect detection in critical shipbuilding components.
- Automated defect identification enhances traceability and structural integrity in naval construction.
- This approach supports efficient quality control in the digital shipbuilding era.
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