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関連する概念動画

Unsymmetric Loading of Thin-Walled Members: Problem Solving01:07

Unsymmetric Loading of Thin-Walled Members: Problem Solving

559
The shear center of a channel section with uniform thickness, height, and width, is determined by computing the shear force in the member and calculating the moments of inertia of the sections.
To compute the shear forces, find the shear flow at a specific distance from the endpoint using the vertical shear and the moment of inertia values. The total shear force on the flange is calculated by integrating the shear flow from one end of the flange to the other.
Next, calculate the moments of...
559
Unsymmetric Loading of Thin-Walled Members01:23

Unsymmetric Loading of Thin-Walled Members

462
Thin-walled members with non-symmetrical cross-sections are vital to engineering structures, offering material efficiency and structural integrity. However, unsymmetrical loading on these members leads to complex stress distributions, resulting in simultaneous bending and twisting can cause deformation or structural failure. The interaction between bending and twisting requires detailed analysis to ensure structural resilience.
The concept of the shear center is crucial in countering the...
462
Lumber Defects01:23

Lumber Defects

603
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...
603
Reducing Line Loss01:18

Reducing Line Loss

408
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
408
Deformation of a Beam under Transverse Loading01:15

Deformation of a Beam under Transverse Loading

825
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...
825
Imperfections in Crystal Structure: Point, Line and Plane Defects01:25

Imperfections in Crystal Structure: Point, Line and Plane Defects

14
A perfect crystal, in theory, has a uniform structure with the same unit cell and lattice points throughout. However, any deviation from this periodic arrangement is known as an imperfection or defect. These defects can be categorized into three types: point, line, and plane defects.Point defects occur when there is a deviation from the ideal due to missing atoms, displaced atoms, or additional atoms. These imperfections might occur due to imperfect packing during crystallization or because of...
14

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関連する実験動画

Updated: Mar 2, 2026

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
07:58

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

Published on: July 25, 2025

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溶接表面欠陥検出のための改良型軽量YOLOv11アルゴリズム

Runmei Zhang1, Chenfei Pan1, Zihua Chen2

  • 1School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, Anhui, 230601, China.

Scientific reports
|March 1, 2026
PubMed
まとめ
この要約は機械生成です。

本研究では、溶接表面欠陥検出を改良した軽量YOLOv11モデルであるYOLO-Airを紹介します。このモデルは、精度を向上させながら、計算負荷とパラメータを大幅に削減し、コスト効率の高いソリューションを提供します。

キーワード:
深層学習欠陥検出PConvYOLOv11

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Last Updated: Mar 2, 2026

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科学分野:

  • コンピュータビジョン
  • 人工知能
  • 材料科学

背景:

  • 産業用溶接は、不明瞭な欠陥特性と複雑な背景という課題に直面しています。
  • 既存の欠陥検出モデルは、多くの場合コストが高く、溶接表面欠陥検出において効率が不足しています。

研究 の 目的:

  • 溶接表面欠陥検出を強化するための改良型軽量YOLOv11モデル（YOLO-Air）を提案すること。
  • 既存モデルのコスト、複雑性、検出精度の限界に対処すること。

主な方法:

  • 特徴抽出と畳み込みモジュールを統合し、特徴表現と効率を向上させました。
  • GSConvおよびVOV-GSCSPモジュールをネックネットワークに組み込み、特徴の冗長性と計算負荷を削減しました。
  • 軽量検出ヘッドを設計し、モデルの複雑性をさらに低減しました。

主要な成果:

  • YOLO-Airモデルは、Welding Defect Test-V2およびNEU-DETデータセットで優れた性能を示しました。
  • mAP50メトリックで1.3%の改善を達成しました。
  • パラメータ数を17.3%、計算複雑性を31.7%削減しました。

結論:

  • 提案されたYOLO-Airモデルは、溶接表面欠陥検出のための効果的かつ効率的なソリューションを提供します。
  • モデルの軽量設計とパフォーマンスの向上により、産業用途に適した選択肢となります。
  • 実験データにより、YOLO-Airモデルのパフォーマンスの堅牢性が確認されました。