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Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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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.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Method of Joints: Problem Solving II01:30

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Consider a truss structure with frictionless joints fixed to a wall and roller support. If a force of 150 N is applied to joint A, the forces in each member of the truss can be determined using the method of joints.
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The method of joints is a commonly used technique to analyze the forces in structural trusses. The method is based on the principle of equilibrium, which assumes that the truss members are connected by frictionless pins. The forces at each joint can be determined by considering the equilibrium of the forces acting on that joint. Consider a truss structure with two forces of 20 N and 10 N acting at joints C and D, respectively. The method of joints can be used to determine the forces FCB, FDC,...
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Three-Dimensional Force System:Problem Solving01:30

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
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Two-Dimensional Force System: Problem Solving01:29

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Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
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Unsymmetric Loading of Thin-Walled Members: Problem Solving01:07

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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.
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Related Experiment Video

Updated: Sep 16, 2025

Surrogate Model Development for Digital Experiments in Welding
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A self-learning method with domain knowledge integration for intelligent welding sequence planning.

Weidong Shen1,2, Xuewen Wang3,4, Juanli Li3,4

  • 1College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China. shenweidong@tyut.edu.cn.

Scientific Reports
|July 10, 2025
PubMed
Summary

A new self-learning method optimizes welding task sequences for mass personalized production. This data-driven approach reduces welding deformation by over 32%, enhancing productivity and flexibility in intelligent welding systems.

Keywords:
Artificial intelligenceHybrid data and knowledge modelIntelligent welding systemSelf-learningWelding-sequence optimizationWelding-task sequencing

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Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Mass personalized production demands higher productivity and flexibility from intelligent welding systems.
  • Current welding-task sequencing methods may not adequately address deformation prediction and optimization.
  • The need for adaptive and intelligent solutions in automated manufacturing is growing.

Purpose of the Study:

  • To develop a self-learning welding-task sequencing method driven by data and knowledge.
  • To create an algorithm for predicting welding deformation accurately.
  • To optimize welding sequences for reduced deformation and improved efficiency in laser welding bracket structures.

Main Methods:

  • Designed a minimized dataset of welding sequences and corresponding deformation data.
  • Developed a predictive algorithm for welding deformation, integrating domain knowledge.
  • Employed a self-learning model for welding-task sequencing and optimization.
  • Validated results using Finite Element Analysis (FEA) as a benchmark.

Main Results:

  • The welding deformation prediction algorithm achieved a maximum relative error of 8% compared to FEA.
  • The proposed self-learning method reduced maximum welding deformation by 32.31% compared to rule-based reasoning.
  • The optimized sequences demonstrated effectiveness for laser welding bracket structures.

Conclusions:

  • The developed data- and knowledge-driven self-learning method is effective for welding sequence planning.
  • This approach enhances the flexibility and productivity of intelligent welding systems.
  • The method offers a significant improvement in reducing welding deformation for complex structures.