---
title: "UTA researcher awarded $500,000 grant to 3D print metamaterials"
url: https://www.voxelmatters.com/uta-researcher-awarded-500000-grant-to-3d-print-metamaterials/
date: 2025-09-02
modified: 2025-09-02
lang: en
author: "Edward Wakefield"
description: "According to the University of Texas at Arlington (UTA), Chen Kan, an assistant professor in the Industrial, Manufacturing, and Systems Engineering Department, has received a Faculty Early Career Development Program..."
categories:
  - "AM Research"
  - "Careers in AM"
tags:
  - "future"
image: https://www.voxelmatters.com/wp-content/uploads/2025/09/chan-career-award-3-640x403.jpg
word_count: 354
---

# UTA researcher awarded $500,000 grant to 3D print metamaterials

[According to the University of Texas at Arlington (UTA)](https://www.uta.edu/news/news-releases/2025/07/28/career-award-for-ai-research), Chen Kan, an assistant professor in the Industrial, Manufacturing, and Systems Engineering Department, has received a Faculty Early Career Development Program award from the National Science Foundation to advance his research and education initiatives.

The award, known as CAREER, is the NSF’s highest honor for junior faculty. It recognizes outstanding researchers who are poised to become leaders in both educational excellence and in the integration of education and research at their home institutions.

The $500,000 grant will enable Dr. Kan to integrate advanced sensing and artificial intelligence to monitor and optimize the additive manufacturing process for [metamaterials - engineered materials](https://www.voxelmatters.com/4d-printed-metamaterials-rutgers/) with mechanical and other properties that are not found in nature.

[Often used in aerospace](https://www.voxelmatters.com/voxelmatters-aerospace-am-focus-2025-ebook/) and health care applications, metamaterials derive their unique properties from precisely fabricated geometries. However, imperfections can occur during the AM process. Some of these flaws may render the final product unusable, while others may have little effect. Kan aims to uncover the complex relationship between imperfections and material properties, enabling companies to identify which defects significantly impact performance - ultimately improving manufacturing decisions and outcomes.

To support his research, Chen Kan will deploy multiple sensing devices to collect data on metamaterials throughout the manufacturing process. As fabrication progresses, [he will use machine learning](https://www.voxelmatters.com/machine-learning-predicts-3d-printed-ti6al4v-performance/) to analyze the data and detect imperfections. His goal is to scale the algorithm for use across a range of processes and materials.

“The algorithm is not for one specific application,” said Kan. “It can be applied to different types of metamaterials. It could really help small- to medium-sized companies involved in metamaterial production, as they won’t have to train an AI model from scratch. They can easily deploy the model and use it to improve their products and make their processes more efficient.”

Kan's research interests include advanced manufacturing, quality control, anomaly detection and machine learning.

“This recognition demonstrates his innovative work in advancing additive manufacturing of property-certified metamaterials. His accomplishments bring distinction to our department and exemplify the strength and impact of our faculty at UTA," said Jeremy Agor, from the College of Engineering at UTA.