REU site: intelligent manufacturing, AI and stimulation

The National Science Foundation Division of Engineering Education and Centers awarded $452,933 to the University of Texas at El Paso on October 1, 2026, to establish a Research Experiences for Undergraduates (REU) Site focused on intelligent manufacturing, artificial intelligence, and digital twin technologies.

The program immerses ten undergraduate students annually in ten weeks of faculty-mentored research integrating additive manufacturing, AI-driven design and process monitoring, and digital twin systems. Students design and fabricate advanced material architectures using 3D-printed ceramics, metals, and polymers; apply machine learning to predict performance and optimize processes; and develop digital twin models for cyber-physical integration. Research thrusts include digital twin-based quality assurance and reverse engineering using high-resolution 3D scanning with AI-driven deviation detection, physics-informed machine learning for energy-absorbing metal lattice design, and molecular dynamics- and machine learning-guided design of polymers for aerospace applications.

The program prioritizes recruitment from community colleges and four-year institutions with limited research infrastructure, aiming to expand participation in engineering research and strengthen the STEM talent pipeline. Award period runs through September 30, 2029, with place of performance in El Paso, Texas. The funding supports work under the NSF Engineering program (CFDA 47.041).

Abstract

Advanced manufacturing technologies that integrate additive manufacturing, artificial intelligence (AI), and digital twin systems are transforming how components are designed, produced, and monitored, with direct implications for aerospace, defense, energy, and semiconductor industries. Meeting this shift requires a workforce trained to work across traditional engineering disciplines and emerging computational tools. This award supports a research experiences for undergraduates (REU) site at the university of texas at EL paso (utep) that immerses ten undergraduate students each year in ten weeks of faculty-mentored research spanning additive manufacturing, AI-driven design and process monitoring, and digital twin technologies. Students design and fabricate advanced material architectures, apply machine learning to predict their performance, and learn to integrate physical experiments with computational models. The program emphasizes recruitment from community colleges and other four-year institutions with limited research infrastructure, expanding participation in engineering research and strengthening the stem talent pipeline needed to keep U.S. Manufacturing innovative and globally competitive. This REU site integrates additive manufacturing, artificial intelligence, and cyber-physical/digital twin systems to address grand challenges in agile, intelligent, and sustainable manufacturing. Faculty-mentored projects span materials-by-design for 3D-printed ceramics, metals, and polymers; AI/ML-driven process monitoring and optimization; and digital twin modeling for cyber-physical integration. Research thrusts include digital twin-based quality assurance and reverse engineering using high-resolution 3D scanning and AI-driven deviation detection; physics-informed machine learning for energy-absorbing metal lattice design; molecular dynamics- and machine learning-guided design of high-char-yield polymers for spacecraft thermal protection; federated learning and digital twin control for resilient semiconductor manufacturing; and bioinspired ceramic-polymer lattice composites with embedded piezoelectric sensing. Students are mentored by faculty across aerospace and mechanical engineering and industrial, manufacturing and systems engineering, supported by utep?s campus office of undergraduate research initiatives (couri) for recruitment, training, and a culminating research symposium, with outcomes assessed by utep?s independent research evaluation and assessment services (reas). The program?s intellectual merit lies in exposing students to the convergence of materials-by-design, machine learning, and cyber-physical systems, while its broader impacts strengthen the stem pipeline for aerospace, defense, semiconductor, and energy sectors aligned with national priorities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.