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Phase Transitions02:31

Phase Transitions

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Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
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Long-term Potentiation01:35

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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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Phase Changes01:19

Phase Changes

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Phase transitions play an important theoretical and practical role in the study of heat flow. In melting or fusion, a solid turns into a liquid; the opposite process is freezing. In evaporation, a liquid turns into a gas; the opposite process is condensation.
A substance melts or freezes at a temperature called its melting point and boils or condenses at its boiling point. These temperatures depend on pressure. High pressure favors the denser form of the substance, so typically, high pressure...
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Action Potential: Phases of Stimulation01:28

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The action potential is a complex electrical event that occurs in excitable cells, such as neurons and muscle cells. It consists of several distinct phases, each with specific characteristics.
Resting Phase:
In this phase, the cell's membrane is at its resting potential, typically around -70 millivolts (mV) for neurons. Inside the cell, there is a higher concentration of potassium ions (K+) and a lower concentration of sodium ions (Na+). Voltage-gated sodium channels are closed, and...
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Time and frequency -Domain Interpretation of Phase-lead Control01:24

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Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...
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Phase Transitions: Sublimation and Deposition02:33

Phase Transitions: Sublimation and Deposition

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Some solids can transition directly into the gaseous state, bypassing the liquid state, via a process known as sublimation. At room temperature and standard pressure, a piece of dry ice (solid CO2) sublimes, appearing to gradually disappear without ever forming any liquid. Snow and ice sublimate at temperatures below the melting point of water, a slow process that may be accelerated by winds and the reduced atmospheric pressures at high altitudes. When solid iodine is warmed, the solid sublimes...
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The Problem of Selecting the Parameters of the Numerical Model of the Heating Process with a Point Heat Source and Its Experimental Verification.

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Algorithm for Determining Time Series of Phase Transformations in the Solid State Using Long-Short-Term Memory Neural

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  • 1Department of Computer Science, Czestochowa University of Technology, Dabrowskiego 73, 42-201 Czestochowa, Poland.

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Summary

This study introduces a novel method using Long-Short-Term Memory (LSTM) neural networks to analyze Continuous Cooling Transformation (CCT) diagrams for metal manufacturing. The research focuses on how LSTM networks store complex material property data, crucial for accurate numerical modeling.

Keywords:
CCT diagramsphase transformationrecurrent neural networktime series

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Artificial Intelligence in Engineering

Background:

  • Numerical analysis of metal manufacturing requires accounting for complex, time-dependent material properties like phase transformation kinetics.
  • Continuous Heating Transformation (CHT) and Continuous Cooling Transformation (CCT) diagrams visualize these properties but present challenges for direct numerical integration.
  • Time series analysis, particularly using neural networks, offers a potential solution for handling the non-linear data within these diagrams.

Purpose of the Study:

  • To present a new approach for storing and analyzing data from Continuous Cooling Transformation (CCT) diagrams using neural networks.
  • To investigate the application of Long-Short-Term Memory (LSTM) neural networks for determining phase fractions based on temperature history.
  • To explore how LSTM network coefficients store information, differentiating between single phase transformation data and comparative network analyses.

Main Methods:

  • Utilized Long-Short-Term Memory (LSTM) neural networks, a type of recurrent neural network, for time series data analysis.
  • Focused on the architecture and coefficients of LSTM networks to understand their data storage capabilities.
  • Investigated the network's ability to store non-linear time series data, prioritizing information retention over generalization.

Main Results:

  • Demonstrated the potential of LSTM networks to store and analyze complex, non-linear time series data from CCT diagrams.
  • Analyzed the effectiveness of LSTM architecture in capturing the history-dependent kinetics of phase transformations.
  • Identified key aspects of LSTM network coefficients related to storing material property information.

Conclusions:

  • LSTM neural networks offer a viable and novel approach for analyzing CCT diagram data in numerical manufacturing simulations.
  • The study highlights the importance of time series analysis for accurately modeling material behavior under varying thermal conditions.
  • Further research can build upon this work to refine LSTM network applications for material property data storage and retrieval.