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Related Concept Videos

Lumber Defects01:23

Lumber Defects

96
Lumber defects, which can affect both the appearance and structural integrity of wood, include a variety of growth and manufacturing flaws. Growth defects such as knots and knotholes occur where branches were once attached to the tree trunk, with knotholes forming when these knots fall out. Other natural defects include decay and insect damage, which compromise the wood's strength and durability.
Shakes are minor fractures that run along or across the wood's annual rings, while wane is...
96
Deleterious Substances in Aggregate01:25

Deleterious Substances in Aggregate

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Deleterious substances in aggregates can be detrimental to the quality and durability of concrete. These substances include organic impurities like loam, which interfere with cement hydration and are usually present in the sand. These prevent a good bond between aggregate and cement paste. Organic impurities can be detected using the colorimetric test, where the darkness of a solution after agitation indicates the level of organic content.
Another type of impurity is clay and fine material that...
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Errors in Taping01:18

Errors in Taping

20
Errors in taping arise from multiple factors that can significantly impact measurement accuracy in surveying. Misalignment of the tape, often due to human error, is one primary source. A skilled rear tapeman, using a telescope, can help correct alignment by guiding the head tapeman; however, human limitations still lead to small inaccuracies. These errors may include misplacement of pins or inaccurate tape readings due to common visual confusions, such as mistaking a six for a nine. Such...
20
Deformation of a Beam under Transverse Loading01:15

Deformation of a Beam under Transverse Loading

238
Understanding beam deflection, particularly for indeterminate beams with overhanging segments and multiple concentrated loads, is crucial for ensuring structural integrity and functionality. The process begins with constructing an accurate free-body diagram, which helps identify the forces and moments acting on the beam. This diagram is vital for visualizing how bending moments vary along the beam's length, influencing its curvature.
The insights from the bending moment diagram extend to...
238
Design Example: Marking Boundaries of a Site Using a Compass01:12

Design Example: Marking Boundaries of a Site Using a Compass

30
Marking site boundaries using a compass is a precise surveying technique that ensures the accuracy of boundary delineation. The process begins by using provided site details, including the bearings and lengths of each boundary line. The initial step involves calculating latitudes and departures for all sides of the site. This computation verifies that the traverse is free of errors, ensuring a closed and accurate boundary.The process starts at a known point, such as Point A, which is often...
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Design Example: Maintaining Level of an Embankment01:19

Design Example: Maintaining Level of an Embankment

48
Constructing a roadway embankment over uneven terrain requires precise leveling to ensure stability and proper drainage. Surveyors use a leveling instrument and staff to calculate ground elevations and determine the required fill material at each point along the embankment alignment.The process begins by positioning a leveling instrument near a benchmark with a known elevation. A backsight reading establishes the instrument height, which serves as a reference for subsequent measurements. A...
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Updated: May 25, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Defect Detection for Enhanced Traceability in Naval Construction.

Paula Arcano-Bea1, Manuel Rubiños1, Agustín García-Fischer1

  • 1Department of Industrial Engineering, University of A Coruña, CTC, CITIC, 15403 Ferrol, Spain.

Sensors (Basel, Switzerland)
|February 26, 2025
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.

Keywords:
anomaly detectionconvolutional autoencoders (CAEs)quality controlshipbuildingunsupervised learning

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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.