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Deformations in a Transverse Cross Section01:21

Deformations in a Transverse Cross Section

182
When a material is subjected to uniaxial stress, it elongates or contracts in the direction of the applied force, and also undergoes changes in the perpendicular directions. This behavior is crucial for understanding how materials behave under stress and is governed by mechanical properties such as Poisson's ratio v, which measures the ratio of transverse strain to axial strain.
As the material stretches, it expands or contracts in orthogonal directions to the load. This phenomenon varies...
182
Deformation of Member under Multiple Loadings01:11

Deformation of Member under Multiple Loadings

163
When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
163
Plastic Deformations of Members with a Single Plane of Symmetry01:21

Plastic Deformations of Members with a Single Plane of Symmetry

87
When a structural member undergoes plastic deformation due to bending, it is crucial to understand the position of the neutral axis and the stress distribution. This member, characterized by a single plane of symmetry, exhibits a uniform stress distribution, with negative stress above the neutral axis and positive stress below. Notably, the neutral axis does not align with the centroid of the cross-section. This misalignment is typical in cases where the cross-section is not rectangular or...
87
Temperature Dependent Deformation01:12

Temperature Dependent Deformation

146
In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...
146
Plastic Deformations01:14

Plastic Deformations

84
It is essential to understand how structural members behave under plastic deformation when the bending stress exceeds the material's yield strength. This state of deformation permanently alters the shape of the member, in contrast to the linear elastic behavior observed before yielding. The strain at any point in the member is expressed in terms of maximum strain. Notably, the neutral axis, which coincides with the centroid during elastic bending, shifts away from the centroid under plastic...
84
Deformations in a Symmetric Member in Bending01:18

Deformations in a Symmetric Member in Bending

165
When analyzing the deformation of a symmetric prismatic member subjected to bending by equal and opposite couples, it becomes clear that as the member bends, the originally straight lines on its wider faces curve into circular arcs, with a constant radius centered at a point known as Point C. This phenomenon helps to understand the stress and strain distribution within the member more clearly.
When the member is segmented into tiny cubic elements, it is observed that the primary stress...
165

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Related Experiment Video

Updated: Jun 18, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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Deformation Estimation of Textureless Objects from a Single Image.

Sahand Eivazi Adli1, Joshua K Pickard2, Ganyun Sun2

  • 1Department of Mechanical Engineering, University of New Brunswick, 15 Dineen Drive, Fredericton, NB E3B 5A3, Canada.

Sensors (Basel, Switzerland)
|July 27, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a novel method for estimating plastic component deformation from a single RGB image. The approach achieves sub-millimeter accuracy on synthetic data and ~2.0 mm on real-world objects, improving 3D geometric inspection.

Keywords:
deformation estimationgraph convolutionimage datasetlabel generationsingle imagetextureless deformed object

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Area of Science:

  • Computer Vision
  • Geometric Deep Learning
  • Manufacturing Metrology

Background:

  • Plastic component production often introduces deformations, compromising 3D geometric accuracy essential for quality control.
  • Accurate 3D inspection is crucial for manufacturing, but deformation estimation from single images remains challenging, especially for textureless objects.

Purpose of the Study:

  • To develop a robust method for estimating deformations in textureless plastic components using a single RGB image.
  • To improve the accuracy of 3D geometric information for object inspection in manufacturing processes.

Main Methods:

  • A unique dataset of five deformed plastic parts was created.
  • A novel sequential deformation method was developed for generating precise mesh labels, outperforming the chamfer distance algorithm.
  • A graph convolution-based training model was employed, projecting object vertices into image features to predict vertex location offsets.

Main Results:

  • The proposed sequential deformation method demonstrated superior performance in generating accurate mesh labels compared to the chamfer distance algorithm.
  • The trained model achieved sub-millimeter accuracy on synthetic images and approximately 2.0 mm accuracy on real-world images for deformation estimation.
  • The method effectively estimates 3D geometric deviations in plastic components from single RGB images.

Conclusions:

  • The proposed single-RGB image-based deformation estimation method offers a practical solution for improving the accuracy of 3D geometric inspection of plastic components.
  • This approach has the potential to enhance quality control in manufacturing by providing precise deformation data.
  • The graph convolution-based model and novel mesh labeling technique represent a significant advancement in handling deformed, textureless objects.