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Frictional Force01:07

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When a body is in motion, it encounters resistance because the body interacts with its surroundings. This resistance is known as friction, a common yet complex force whose behavior is still not completely understood. Friction opposes relative motion between systems in contact, but also allows us to move. Friction arises in part due to the roughness of surfaces in contact. For one object to move along a surface, it must rise to where the peaks of the surface can skip along the bottom of the...
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Friction: Problem Solving01:21

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Friction is an essential force that influences the motion of objects in daily life. Depending on the situation, it can be either beneficial or problematic. Consider a bus with a mass of three megagrams and its center of mass at a specific point, moving along a banked road at a constant speed. The coefficient of static friction between the tires and the road is 0.5. Find the maximum angle of the banked road at which the bus would not slip or tip.
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Dry Friction01:30

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Dry friction occurs between two solid surfaces in contact as they attempt to move relative to one another. In daily life, dry friction is encountered in various forms, such as when walking on the ground, sliding an object across a table, or rubbing hands together. Despite its ubiquity, the underlying mechanisms behind dry friction are not readily visible.
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Frictional Forces on Flat Belts01:28

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Flat belts are commonly used in various industrial applications for transmitting power from one pulley to another. When a flat belt is wrapped around a set of pulleys, it experiences different tensions at the driving pulley ends due to the friction between the belt and pulley surface. When the pulley moves in a counterclockwise direction, the tension T2 on the opposite side of the pulley where the belt is moving away from is higher than the tension T1 on the side where the belt is moving...
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Static and Kinetic Frictional Force01:05

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One of the simpler characteristics of sliding friction is that it is parallel to the contact surfaces between systems, and is always in a direction that opposes the motion or attempted motion of the systems relative to each other. If two systems are in contact and moving relative to one another, then the friction between them is called kinetic friction. For example, kinetic friction slows a hockey puck sliding on ice.
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Characteristics of Dry Friction01:21

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Dry friction occurs when two solid surfaces slide against each other without any lubrication or fluid present. It causes resistance when pushing objects along a surface, like a gardener pushing a wheelbarrow. The force applied to move the cart causes dry friction between the wheel and the ground.
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Updated: Sep 8, 2025

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Explorando el comportamiento mecánico de los materiales compuestos de fricción utilizando inteligencia artificial

D Matté1, C A Perottoni1

  • 1Universidade de Caxias do Sul, Caxias do Sul - RS, 95070-560, Brazil.

Neural networks : the official journal of the International Neural Network Society
|September 5, 2025
PubMed
Resumen

Los modelos de inteligencia artificial (IA) predicen las propiedades del material de fricción a partir de la composición química. Las redes neuronales ofrecen una precisión superior a los modelos multilineos para las predicciones de nuevos materiales.

Palabras clave:
Inteligencia artificialMateriales de fricciónAprendizaje automático

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Área de la Ciencia:

  • Ciencias de los materiales
  • Ciencias de los materiales computacionales
  • Tribología

Sus antecedentes:

  • La inteligencia artificial (IA) ofrece soluciones avanzadas para problemas complejos en ciencia e ingeniería.
  • Los métodos tradicionales para desarrollar nuevos materiales, como los compuestos de fricción, a menudo implican altos costos y una amplia experimentación.
  • La IA puede optimizar el diseño de materiales y predecir propiedades con mayor eficiencia.

Objetivo del estudio:

  • Desarrollar modelos matemáticos basados en IA para predecir las propiedades mecánicas de los materiales de fricción en función de su composición química.
  • Crear un algoritmo para optimizar las composiciones de materiales de fricción existentes y proponer nuevas.
  • Evaluar la precisión predictiva de los modelos de IA en comparación con los métodos tradicionales.

Principales métodos:

  • Utilizó una extensa base de datos de composiciones químicas y propiedades mecánicas de los materiales de fricción.
  • Desarrolló un algoritmo de IA que combina sistemas basados en reglas, redes neuronales y optimización de enjambres de partículas.
  • Produjo y probó muestras físicas basadas en predicciones de algoritmos para validar la precisión del modelo.

Principales resultados:

  • Los modelos de IA, particularmente las redes neuronales, demostraron una precisión predictiva significativamente mayor para las nuevas composiciones de materiales de fricción.
  • El error de la raíz media cuadrada (RMSE) para las predicciones de redes neuronales fue sustancialmente menor que para los modelos multilineos.
  • En promedio, los modelos multilineos mostraron un RMSE 48.6% más grande en comparación con las predicciones de IA para composiciones inexistentes.

Conclusiones:

  • La IA, específicamente las redes neuronales, proporciona un enfoque superior para predecir las propiedades mecánicas de los nuevos materiales de fricción.
  • El algoritmo desarrollado optimiza efectivamente la composición del material y propone nuevas formulaciones.
  • El modelado basado en IA reduce la necesidad de costosos experimentos físicos y acelera el descubrimiento de materiales.