91暗网

The focus of the Auraria Campus Advanced Manufacturing University Center program is to conduct research and offer technical assistance through applied research and partnership to all parts of the State including underserved areas around the Auraria campus. This University Center helps the manufacturing industry develop new products or create new and better manufacturing processes, on a fee-for-service basis, using Artificial Intelligence (AI) and Machine Learning (ML) technologies.

Metropolitan State University of Denver

University of Colorado Denver

Colorado School of Mines

91暗网 has partnered with Reata Engineering & Machine Works and Big Metal Additive to address key problems in aligning with industry needs. Partnership activities include exchange of expertise, best practices, and lessons learned in areas of mutual interest such as algorithm development.

Reata Engineering & Machine Works sponsored two Senior Design projects for mechanical engineering students with the opportunity to work on real-world challenges. This partnership bridged the gap between academia and industry, offering students hands-on experience while delivering innovative solutions to industrial problems.

The 91暗网 team prepared survey questions and investigated how to integrate AI techniques in manufacturing. The study aimed to provide insights into how AI/ML is transforming manufacturing processes, enhancing efficiency, and supporting data-driven decision-making.

In July 2024, the聽 University partners hosted a workshop on Additive Manufacturing to provide an interdisciplinary platform for students, researchers, and industry professionals to explore the latest technologies, trends, research findings, and applications in the field of Additive Manufacturing (AM).

Dr. Devi Kalla presented on the topic “AI in Digital Manufacturing”.

The acquisition of an Xact Metal Accessible Metal Powder-Bed Fusion 3D printer will enhance the University Center’s research capabilities in additive manufacturing and provide valuable training opportunities for students, researchers and industry partners. The printer will help researchers develop new machine learning models with instrinsic interpretability and increased adaptablity to support metal additive manufacturing.

In high-performance mechanical systems, efficient thermal management and mechanical load support are critical for reliability and performance. This project aimed to design, prototype, and evaluate a conical bearing heat exchanger that maximizes heat transfer while supporting mechanical loads under real-world operating conditions.

The Student Team developed a unique conical bearing structure to optimize surface area and fluid dynamics for improved heat transfer integrating compact design principles in aerospace, automotive, and industrial machinery. The Team achieved a 20鈥�30% improvement in heat transfer efficiency compared to traditional cylindrical configurations (validated via prototype testing).

The Team successfully fabricated a working prototype using CNC machining and high-temperature-resistant materials. The project was selected for presentations at the聽National Conference for Undergraduate Research (NCUR) 2025,

The sport of combat robotics is always evolving which provides new and unique challenges to overcome each year. The Student Team was tasked with designing and building a combat robot to compete in conduct by National Robotics League Colorado. The robot had to conform to all competition rules, most importantly the weight and safety rules set by Reata Engineering. The Team used knowledge of kinematics, material science, Additive Manufacturing, CAD, and innovation to improve on past years鈥� robots.

The Team presented this project to the Engineering and Enginnering Technology Symposium in May 2025.

Achieving the desired surface texture within specified tolerances remains challenging due to the complex interplay of parameters such as laser power, scanning speed, layer thickness, and powder characteristics in powder-bed fusion systems. Manufacturers must often rely on time-consuming and costly trial-and-error approaches to identify optimal settings, which increases production lead times and costs. This project sought to address these challenges by exploring the use of artificial intelligence (AI), specifically machine learning (ML) models, to predict surface roughness in metal additive manufacturing. By leveraging the predictive power of AI and ML algorithms, the project aims to develop a data-driven framework capable of modeling and predicting how exact process parameters impact surface quality with a goal of moving away from traditional trial-and-error methods toward a more efficient, automated approach where surface roughness can be predicted with high accuracy before printing begins.

After extensive research, the Research Team identified four input parameters and two output parameters for the research to develop Machine Learning based Algorithm. The input parameters are Hatch spacing, Angle, Layer Height and Rake blade angle (Aft and Fore) with Ra and Rz as output parameters. The Team designed experiments with 22 specimens metal printing. The CAD model is loaded into a NetFab software that slices the model and translates the model from a 3D object into a command instruction for the metal 3D printer. From here, the parameters that the team can change will be conducted, those parameters involve things such as the layer height, and hatch spacing.

The Annual Conference is the single largest gathering of the year for ASEE members. For many who attend the conference, it is their best opportunity to share their individual and collective work in engineering education.

Dr. Devi Kalla presented research in a presentation entitled Adoption of Digital Twin and Artificial Intelligence in Metal Additive Manufacturing 鈥� Current Status and Vision for Future.

As part of our ongoing efforts to promote undergraduate research and professional development under the EDA- funded initiative, we are proud to report that our student-led projects were selected for presentations at the National Conference for Undergraduate Research (NCUR) 2025, April 7-9th at Pittsburgh, PA.聽This prestigious recognition reflects the quality and impact of the experiential learning opportunities made possible by the grant.

The selected project, titled 鈥淧redictive Modeling of Surface Roughness in Metal Additive Manufacturing using Machine Learning鈥�, focusd on Machine learning algorithm to predict surface roughness in metal additive manufacturing, and was conducted under the mentorship of Dr. Devi Kalla, with support from the EDA program鈥檚 resources. The students involved presented their research to a national audience of peers, faculty, and industry experts, gaining valuable experience in scholarly communication and networking. Their participation helps raise the visibility of our University Center commitment to economic development and innovation.

This accomplishment underscores the grant鈥檚 impact not only on institutional capacity-building, but also on student success and regional engagement.