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Related Concept Videos

Deformation in a Circular Shaft01:10

Deformation in a Circular Shaft

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One of the distinctive characteristics of circular shafts is their ability to maintain their cross-sectional integrity under torsion. In other words, each cross-section continues to exist as a flat, unaltered entity, simply rotating like a solid, rigid slab. To understand the distribution of shearing stress within such a shaft, consider a cylindrical section inside this circular shaft. This section has a length of L and a radius of R, with one end fixed. The radius of the cylindrical section is...
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When materials are subjected to forces that surpass their yield strength, they undergo a process known as plastic deformation. This results in a permanent alteration or strain in their structure. This concept can be specifically applied to circular shafts, where the deformation leads to a change in its shape. The precise evaluation of this plastic deformation requires understanding the stress distribution within the circular shaft, which is achieved by calculating the maximum shearing stress in...
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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.
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Sequence Networks of Rotating Machines01:24

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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When analyzing elongated structures like bars subjected to uniformly distributed loads, it is essential to understand the transformation of plane strain when coordinate axes are rotated. This transformation helps to assess how material deformation characteristics vary with orientation, which is crucial in materials science and structural engineering.
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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Reinforcement learning method for machining deformation control based on meta-invariant feature space.

Yujie Zhao1, Changqing Liu2, Zhiwei Zhao1

  • 1College of Mechanical and Electrical Engineering/National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.

Visual Computing for Industry, Biomedicine, and Art
|November 23, 2022
PubMed
Summary

This study introduces a novel reinforcement learning method for controlling machining deformation in aerospace manufacturing. The approach effectively manages variations in residual stress, improving component quality.

Keywords:
Deformation controlMachining deformationMeta-invariant feature space; Reinforcement learningResidual stress

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Area of Science:

  • Manufacturing Engineering
  • Artificial Intelligence
  • Materials Science

Background:

  • Machining deformation control is vital for aerospace component quality.
  • Variations in residual stress across different blank batches challenge precise deformation control.

Purpose of the Study:

  • To develop a reinforcement learning (RL) method for machining deformation control.
  • To address challenges posed by varying residual stress distributions in manufacturing.
  • To enhance the quality of structural aerospace components.

Main Methods:

  • A reinforcement learning model was developed for dynamic machining process control.
  • The method utilizes a meta-invariant feature space to learn relationships across different stress distributions.
  • Deformation force monitoring was employed for real-time control adjustments.

Main Results:

  • The proposed RL method demonstrated effective machining deformation control for different blank batches.
  • The meta-invariant feature space enabled adaptation to varying internal stress conditions.
  • Experimental results confirmed superior performance compared to existing methods.

Conclusions:

  • The developed RL method offers a robust solution for machining deformation control.
  • This approach improves manufacturing quality by adapting to residual stress variations.
  • The study highlights the potential of AI in advanced manufacturing processes.