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How to Use Kinetics in Phase Transformations Using Matlab

June 21, 2024
Johann Schmidt
Johann Schmidt
Johann Schmidt has over 15 years of experience in materials science and engineering, graduating from the University of Vienna, Austria, with a Ph.D.

In materials science and engineering, phase transformations play a pivotal role, particularly in disciplines like metallurgy, where tailoring material properties is critical. The Johnson-Mehl-Avrami (JMA) equation is a foundational tool for quantifying these transformations. It enables researchers to model how materials change over time as they transition between different phases, such as from austenite to pearlite in steel. By incorporating factors such as nucleation rate and growth kinetics, the JMA equation provides a mathematical framework to predict the fraction of material transformed at any given time. This predictive capability is essential for designing alloys with desired mechanical, thermal, and structural characteristics. Whether in industrial applications or academic research, understanding and applying the JMA equation empowers scientists and engineers to innovate in material synthesis, processing, and application, driving advancements across diverse sectors including automotive, aerospace, and renewable energy technologies. This highlights the importance of the JMA equation in both theoretical studies and practical implementations, demonstrating how it underpins the desired properties to engineered materials. Understanding the JMA equation and its application will be crucial for effectively utilizing MATLAB to model and analyze phase transformations in materials science.

The Johnson-Mehl-Avrami Equation: A Key Tool in Materials Science

Matlab Kinetics Phase Transformations

The Johnson-Mehl-Avrami (JMA) equation stands as a cornerstone in the study of phase transformations within materials science. It provides a robust mathematical foundation to characterize the evolution of materials during phase changes over time. Whether applied to predict crystallization kinetics, solid-state transformations, or phase transitions, the JMA equation elucidates how the fraction of transformed material evolves with time. By incorporating parameters such as nucleation rate, growth rate, and time, the equation captures the complex interplay between these factors, offering invaluable insights into the kinetics of transformation processes. This predictive capability is essential in fields ranging from metallurgy to ceramics, enabling engineers and researchers to optimize processing conditions, design materials with tailored properties, and advance innovations in manufacturing technologies. Thus, the JMA equation not only aids in theoretical understanding but also drives practical applications that shape the development and utilization of materials across various industries.

Exploring the JMA Equation

The JMA equation is expressed as:

f(t)=1−exp⁡(−π3(dNdt)v3t4)f(t) = 1 - \exp \left( -\frac{\pi}{3} \left( \frac{dN}{dt} \right) v^3 t^4 \right)f(t)=1−exp(−3π(dtdN)v3t4)


  • f(t)f(t)f(t) represents the fraction of material transformed at time ttt,
  • dNdt\frac{dN}{dt}dtdN denotes the nucleation rate,
  • vvv signifies the growth rate,
  • ttt stands for time.

This formula encapsulates the interplay between nucleation and growth rates in determining how quickly and to what extent a material undergoes phase transformation.

Solving a Practical Problem: Austenite to Pearlite Transformation

To illustrate the application of the JMA equation, let's consider a specific problem involving a plain-carbon steel undergoing transformation from austenite to pearlite at two different temperatures.

Problem Setup

Suppose we have:

1. Transformation at 700 °C:

o dNdt=6.31×10−4\frac{dN}{dt} = 6.31 \times 10^{-4}dtdN=6.31×10−4 nuclei per mm3\text{mm}^3mm3 per second

o v=3.16×10−4v = 3.16 \times 10^{-4}v=3.16×10−4 mm per second

2. Transformation at 550 °C:

o dNdt=1000\frac{dN}{dt} = 1000dtdN=1000 nuclei per mm3\text{mm}^3mm3 per second

o v=8.91×10−3v = 8.91 \times 10^{-3}v=8.91×10−3 mm per second

Step-by-Step Solution Using Matlab

Let's delve into how we can use Matlab to solve this problem effectively:

Step 1: Define Constants and Variables

In Matlab, start by assigning the given values to constants and setting up the time range for calculations.

% Constants for transformation at 700 °C

dNdt1 = 6.31e-4; % nuclei per mm^3 per s

v1 = 3.16e-4; % mm per s

% Constants for transformation at 550 °C

dNdt2 = 1000; % nuclei per mm^3 per s

v2 = 8.91e-3; % mm per s

% Time range (logarithmically spaced)

t = logspace(-3, 4, 100); % time values from 10^-3 to 10^4 seconds

Step 2: Calculate Fraction Transformed f(t)f(t)f(t)

Implement the JMA equation to compute f(t)f(t)f(t) for both temperatures over the defined time range.

% Compute fraction transformed using JMA equation

f1 = 1 - exp(-(pi/3) * dNdt1 * v1^3 * t.^4); % at 700 °C

f2 = 1 - exp(-(pi/3) * dNdt2 * v2^3 * t.^4); % at 550 °C

Step 3: Plot the Transformation Curves

Visualize the transformation kinetics by plotting f(t)f(t)f(t) against log⁡t\log tlogt for both temperatures using Matlab's plotting capabilities.

% Plotting


semilogx(t, f1, 'b-', t, f2, 'r--', 'LineWidth', 2);

xlabel('log t (seconds)');

ylabel('Fraction transformed, f(t)');

title('Phase Transformation Kinetics');

legend('700 °C', '550 °C', 'Location', 'best');

grid on;

Step 4: Finding Transformation Times

Lastly, determine the times required to achieve specific levels of pearlite transformation (e.g., 1% and 99%) at each temperature.

% Finding times for 1% and 99% pearlite transformation

t_700_1percent = interp1(f1, t, 0.01);

t_700_99percent = interp1(f1, t, 0.99);

t_550_1percent = interp1(f2, t, 0.01);

t_550_99percent = interp1(f2, t, 0.99);

fprintf('Times for 1%% pearlite at 700 °C: %.2f seconds\n', t_700_1percent);

fprintf('Times for 99%% pearlite at 700 °C: %.2f seconds\n', t_700_99percent);

fprintf('Times for 1%% pearlite at 550 °C: %.2f seconds\n', t_550_1percent);

fprintf('Times for 99%% pearlite at 550 °C: %.2f seconds\n', t_550_99percent);


Understanding and applying the Johnson-Mehl-Avrami equation in Matlab allows engineers and researchers to predict phase transformation kinetics accurately. By plotting transformation curves and calculating transformation times, one can gain insights into how different parameters affect material properties over time.

This approach not only aids in solving specific assignments but also equips students with valuable skills applicable across various materials science and engineering disciplines.

Phase transformations are a fundamental aspect of materials engineering, influencing the mechanical, thermal, and chemical properties of materials. The ability to model and predict these transformations is invaluable for optimizing manufacturing processes, improving material performance, and designing new materials with tailored properties.

In this blog post, we've explored the Johnson-Mehl-Avrami equation, a powerful tool for describing phase transformations, and demonstrated its application through a practical example using Matlab. By following the outlined steps and utilizing Matlab's computational capabilities, students and researchers can effectively analyze and simulate phase transformation kinetics in various materials systems.

For students and professionals alike, mastering the use of Matlab for kinetics in phase transformations not only enhances technical proficiency but also opens doors to new research opportunities and practical applications in materials science and engineering. Whether you're studying steel alloys, ceramics, polymers, or composites, understanding these principles and methods will undoubtedly prove beneficial throughout your career.

By continually exploring and applying computational tools like Matlab, we contribute to the ongoing advancement of materials science and engineering, driving innovation and solving complex challenges in industries ranging from aerospace and automotive to electronics and biomedical devices.

In conclusion, the journey of mastering phase transformation kinetics in Matlab is not just about solving assignments; it's about developing a deep understanding of material behavior and leveraging computational tools to push the boundaries of what's possible in materials engineering.

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