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<!DOCTYPE html>
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<title>SPS Parameter Identification | Ashwani Kumar</title>
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content="A two-stage approach for material parameters identification in spark plasma sintering using COMSOL Multiphysics.">
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<h2 class="h2 article-title">Multiphysics Simulations of Spark Plasma Sintering Process</h2>
</header>
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<img src="./assets/images/sps_process.png" alt="SPS Process Simulation" loading="lazy">
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<h4 class="h4">Two-Stage Framework for Material Parameter Identification in Spark Plasma Sintering</h4>
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<span class="info-label">Main Author:</span>
<span class="info-value">Ashwani Kumar</span>
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<span class="info-label">Affiliation:</span>
<span class="info-value">ETH Zurich</span>
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<span class="info-label">Online:</span>
<a href="https://doi.org/10.1016/j.jmapro.2023.12.001" target="_blank"
class="info-value text-link">ScienceDirect</a>
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<span class="info-label">Co-Authors:</span>
<span class="info-value">M. Bernet, L. Deillon, M. Bambach, M. Afrasiabi</span>
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<span class="info-label">Date:</span>
<span class="info-value">2024</span>
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<span class="info-label">Journal:</span>
<span class="info-value">Journal of Manufacturing Processes</span>
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<h4 class="h4">Project Overview</h4>
<p>
This project addresses a major challenge in spark plasma sintering (SPS): the accurate identification of
material parameters needed for finite element simulations.
We developed a novel two-stage numerical–experimental framework that uses COMSOL Multiphysics and
genetic algorithm optimization to identify thermal, electrical, and mechanical parameters with high
precision.
</p>
<h4 class="h4">Problem & Motivation</h4>
<p>
Accurate SPS simulations require precise material data (thermal conductivity, electrical resistivity,
creep
parameters) which are often temperature-dependent and difficult to measure directly during the process.
Traditional calibration methods are often ad-hoc or assume constant values, leading to significant
simulation errors.
</p>
<h4 class="h4">Research Contributions</h4>
<ul class="feature-list">
<li>Developed a two-stage identification strategy separating electro-thermal calibration from mechanical
calibration.</li>
<li>Used genetic algorithms (GA) coupled with FEM to minimize error between simulation and experimental
data.</li>
<li>Validated the identified parameters for both Graphite (mold material) and Copper/Nickel powders.</li>
<li>Achieved a relative density prediction error of less than 2.3% for unseen heating rates.</li>
<li>Provided a transferable methodology applicable to any conductive powder system in SPS.</li>
</ul>
<h4 class="h4">Technical Highlights</h4>
<ul class="tech-list">
<li>Tools & Platforms: COMSOL Multiphysics, MATLAB (for Optimization), Genetic Algorithms.</li>
<li>Methods: Inverse parameter identification, Finite Element Analysis (FEA), Creep modeling (Norton law,
Shima-Oyane).</li>
<li>Validation: Compared simulated temperature and displacement curves with experimental pyrometer and
LVDT data.</li>
</ul>
<h4 class="h4">Results & Impact</h4>
<p>
The framework enables highly accurate predictive modeling of the SPS process, reducing the need for
expensive experimental trial-and-error.
By accurately capturing the temperature-dependent evolution of material properties, the model can predict
final density and shape with high fidelity, facilitating the optimization of sintering cycles for new
materials.
</p>
<h4 class="h4">Publication Details</h4>
<p>
<strong>Journal:</strong> Mechanics of Materials<br>
<strong>Year:</strong> 2023<br>
<strong>DOI:</strong> <a href="https://doi.org/10.1016/j.mechmat.2023.104834" target="_blank"
class="text-link">10.1016/j.mechmat.2023.104834</a>
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