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Functional Classification of Joints01:09

Functional Classification of Joints

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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.
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The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
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Sieve analysis is a method used to determine the particle size distribution of aggregate materials. This process involves the following steps:
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Abrasion Resistance of Concrete01:23

Abrasion Resistance of Concrete

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Abrasion resistance is an essential characteristic of concrete that determines its durability and longevity under various wear conditions. Concrete surfaces are vulnerable to different types of abrasion. For instance, surfaces may wear down due to the constant movement of vehicles or be eroded by solids carried in water, as seen in concrete canal linings. Specific tests are conducted to measure the abrasion resistance of concrete.
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End Point Prediction: Gran Plot01:07

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Types of Aggregate Grading01:15

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Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
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Wear Prediction of Functionally Graded Composites Using Machine Learning.

Reham Fathi1, Minghe Chen1, Mohammed Abdallah2

  • 1College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

Materials (Basel, Switzerland)
|September 28, 2024
PubMed
Summary
This summary is machine-generated.

Functionally graded composites from magnesium waste and eggshells show improved hardness and wear resistance. Machine learning models, particularly LightGBM, accurately predict wear behavior, reducing the need for extensive testing.

Keywords:
functionally graded compositeslow-cost eggshell reinforcementmachine learningmagnesium chipswearworn surface

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Area of Science:

  • Materials Science and Engineering
  • Composite Materials
  • Tribology

Background:

  • Magnesium matrix composites offer potential for lightweight applications.
  • Utilizing waste materials like magnesium chips and eggshells addresses environmental concerns and reduces costs.
  • Functionally graded materials (FGMs) are desirable for applications requiring tailored properties across a component.

Purpose of the Study:

  • To produce functionally graded composites using recycled magnesium and eggshell reinforcements via centrifugal casting.
  • To investigate the wear behavior of these composites under varying loads, speeds, and distances.
  • To develop and compare machine learning models for predicting the wear rate of these FGMs.

Main Methods:

  • Centrifugal casting was employed to create functionally graded composites with eggshell particles in a magnesium matrix.
  • Wear tests were conducted across a range of loads, sliding speeds, and distances.
  • Four machine learning algorithms were trained and evaluated on experimental data to predict wear rate.

Main Results:

  • Eggshell particles were successfully integrated in graded levels, increasing hardness by 25.26% and wear resistance by 19.8% in the outer zones compared to inner zones.
  • Tree-based machine learning models outperformed deep neural networks in predicting wear rate.
  • The LightGBM model demonstrated the highest accuracy in predicting wear rate across different zones.

Conclusions:

  • Recycled magnesium waste chips and eggshell particles can be effectively used to create cost-effective, functionally graded composites.
  • The developed machine learning models offer an efficient method for evaluating FGM wear behavior, minimizing the need for extensive physical testing.
  • These FGMs are suitable for automotive components requiring specific hardness and wear resistance profiles, promoting sustainable material utilization.