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Related Concept Videos

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

Unsymmetric Loading of Thin-Walled Members: Problem Solving

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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.
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.
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Structural Classification of Joints01:20

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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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Functional Classification of Joints01:09

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
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Unsymmetric Loading of Thin-Walled Members01:23

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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...
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Method of Joints: Problem Solving II01:30

Method of Joints: Problem Solving II

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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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Method of Joints: Problem Solving I01:30

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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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Multi-objective optimisation of ultrasonically welded dissimilar joints through machine learning.

Patrick G Mongan1,2, Vedant Modi2, John W McLaughlin2

  • 1Confirm Smart Manufacturing Research Centre, Limerick, Ireland.

Journal of Intelligent Manufacturing
|March 21, 2022
PubMed
Summary

Ultrasonic welding of carbon fibre composites was optimized using a hybrid genetic algorithm-artificial neural network (GA-ANN). This advanced technique achieved a 3% prediction error for optimal lap shear strength, enhancing composite joining processes.

Keywords:
Artificial neural networkBayesian optimisationDissimilar materialsGenetic algorithmMachine learningUltrasonic welding

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Area of Science:

  • Materials Science and Engineering
  • Manufacturing Processes
  • Computational Materials Science

Background:

  • Composite materials offer advantages like high strength-to-weight ratio and corrosion resistance, driving their increased use in renewable energy, transport, and construction.
  • Fusion joining techniques for composites are not well-understood, presenting a knowledge gap for industrial applications.
  • Ultrasonic welding is a promising fusion joining method for dissimilar composite materials.

Purpose of the Study:

  • To investigate the influence of ultrasonic welding parameters on the lap shear strength (LSS), repeatability, and defects of carbon fibre/PEKK to carbon fibre/epoxy joints.
  • To develop a robust machine learning model for optimizing the ultrasonic welding process.
  • To validate the optimized process through experimental testing.

Main Methods:

  • A 3^3 parametric study was conducted on ultrasonic welding of carbon fibre/PEKK to carbon fibre/epoxy composites.
  • A hybrid genetic algorithm-artificial neural network (GA-ANN) model was developed and trained on experimental data.
  • Bayesian optimization was used to tune GA-ANN hyperparameters, and the surrogate model optimized welding parameters for LSS, repeatability, and visual quality.

Main Results:

  • The GA-ANN model, optimized via Bayesian optimization, successfully predicted optimal welding parameters.
  • The optimized ultrasonic welding process achieved a prediction error of only 3% for lap shear strength (LSS) when validated experimentally.
  • The study demonstrated the effectiveness of machine learning for optimizing composite joining processes.

Conclusions:

  • Ultrasonic welding is a viable technique for joining dissimilar carbon fibre composites (PEKK and epoxy).
  • A hybrid GA-ANN model effectively optimizes complex welding processes, improving lap shear strength and repeatability.
  • This research provides a data-driven approach to enhance the industrial application of composite fusion joining.