Research projects

Undergraduate research projects cross-cut critical areas in smart and sustainable manufacturing and are classified into two types: Type I - Systems from product and process up to plants, industrial zones, and beyond and Type II - Systems from product and process down to material design, molecular characterization, and atomic characterization.

Type I - Systems from product and process up to plants, industrial zones and beyond

Improving product disassembly through digital twin based smart human-collaborative robot dismantling

Faculty mentor: Dr. J. L. Rickli | Disciplines: Mechanical Engineering, Industrial & Systems Engineering

As one of the first stages of circular economy reverse cycles, disassembly is critical to establishing cost effective and efficient sustainable circular economies. Disassembly performance suffers due to uncertainty caused by an unstable end-of-use product supply (e.g., multi-source, multi-product, or missing orders) and unpredictable quality (e.g., damage or low value). These challenges are compounded by the dependence of on-the-job training and the lack of resources for formal training programs for disassembly and remanufacturing skills. A smart manufacturing, human-centric strategy that integrates Artificial Intelligence (AI), collaborative robots (cobots), and eXtended Reality (XR) can scale-up disassembly operations, contributing to enhancing the sustainability of product/process systems. In these systems, cobots provide assistive services to operators and XR bridges the gap between data science/analytics and operator actions. In this project Site participants will contribute to Dr. Rickli’s Manufacturing and Remanufacturing Systems Laboratory (MaRSLab) research into Perceptive Disassembly systems, which experiments with the use of novel Digital Twins, AI, XR, and collaborative robotic approaches in dismantling operations. Specifically, Site participants will perform dismantling experiments using the Perceptive Disassembly system to study the system’s capability to digitally capture dismantling information and recommend dismantling actions.

Matching design and manufacturing knowledge via entrepreneurial service ecosystem

Faculty mentor: Dr. K-Y Kim | Disciplines: Industrial & Systems Engineering, Mechanical Engineering

This project aims to promote entrepreneurial manufacturing services and ecosystems by utilizing an extensible micro-service architecture. To realize such an ecosystem, a service platform should facilitate the matchmaking of design information and specifications with sustainable and scalable manufacturing knowledge. Ad hoc and teamed efforts in computer science, computational design, design automation, and manufacturing engineering, along with dedicated contributions from industry professionals and entrepreneurs, have advanced various aspects of reshaping manufacturing practices into a more effective system capable of meeting the diverse and ever-changing demands of customers. Current technology stands on the verge of realizing this potential. The resulting service platform from this project empowers customers to connect with manufacturer supply chains for the creation of the demanded products. This connection is facilitated through the development of AI and knowledge graph algorithms that extract and match customer-design specifications, which are obtained from heterogeneous sources with relevant manufacturing information. Entrepreneurial manufacturers will experience enhanced accessibility to their products, services, and production resources through this service platform. This project showcases the platform's capabilities with customizable PPE and rapidly demanded products, which often have prohibitively high unit costs, limiting accessibility for many customers. The REU students will gain insight into how sustainability considerations in design and manufacturing decisions influence the matchmaking processes and the realization of such a service-oriented manufacturing platform and the ecosystem.

Intelligent human–robot collaboration in manufacturing via virtual reality

Faculty mentor: Dr. S. Masoud | Disciplines: Industrial & Systems Engineering, Mechanical Engineering, Computer Science

The integration of Human-Robot Collaboration (HRC) systems in manufacturing and service industries has the potential to significantly enhance productivity and efficiency. However, these systems present unique challenges, particularly in managing the dynamics of a turning workforce, characterized by the frequent entry and exit of workers with varying skill sets and experience levels. To address these challenges, we propose the development of an Augmented Reality (AR) model tailored for workforce management in HRC environments. This AR model aims to provide real-time training, guidance, and feedback to workers, seamlessly adapting to their skill level and optimizing collaborative interactions with robots. The proposed model leverages AR technology to overlay contextual information, such as safety guidelines, task instructions, and performance metrics, directly into the user's field of view. By facilitating rapid skill acquisition and minimizing errors, the model ensures smoother human-robot collaboration and enhances overall system efficiency. Our research will involve the design, implementation, and evaluation of the AR model, focusing on its impact on task performance, worker engagement, and system adaptability. The findings are expected to contribute to the broader understanding of how AR can be effectively utilized in dynamic, technology-driven workspaces, ultimately advancing the field of smart manufacturing.

Sustainable mobility technologies for affordable electric vehicles

Faculty mentor: Dr. S. Wang | Disciplines: Electrical Engineering, Mechanical Engineering

The metal finishing industry is critical to many manufacturing industries, such as automotive, aerospace, electronics, defense, as well as a variety of OEMs. Over the past two decades, low-cost imports from overseas and other globalization trends have significantly impacted the industry. According to an EPA report in 2007, job loss was in the range of 25-30% between 2000 and 2003, with a corresponding reduction in sales of approximately 40%. The industry has been also highly regulated by EPA for decades due to its significant use of numerous toxic/hazardous chemicals and the generation of huge amounts of waste in various forms. In this project, an REU student will learn sustainability concepts and sustainability assessment methods, analyze various plant data and technology performance, and then perform sustainability performance evaluation of selected electroplating systems, and identify most suitable technical solutions for improving shot-to-long-term sustainability performance.

Process Mannerisms Toward Data Visualization in an Industry 4.0 Framework

Faculty mentor: Dr. H. Durand | Disciplines: Chemical Engineering, Mechanical Engineering, Industrial & Systems Engineering

With interest in the Internet of Things (IoT) as part of the Industry 4.0 framework, it becomes important to decide how to analyze large amounts of manufacturing data to notice anomalies or changes that could impact product quality or safety, both of which are important components of sustainability objectives. In the process industries, methods of understanding how well a process is operating from data include process monitoring approaches facilitating fault detection and diagnosis. However, the potential for larger amounts of data at a plant becomes more prevalent raises the question of whether new methods of visualizing process behavior may aid with anomaly detection and diagnosis, and how such methods would compare with established ones. This project aims to develop a process monitoring framework based on this concept and then to compare it with traditional monitoring approaches. Two process models will be selected to test the developed methods. REU students will utilize the open-source 3D graphics software Blender to develop 3D models and their rigs that could be used to represent a process. An initial mapping between the states and inputs of the process and the movable components of the rig will be made. An attempt will be made to locate states and inputs which have the most impact on one another close together physically on the character or structure. Subsequently, the model will be associated with the process simulation under control using Blender’s Python interface. Anomalies such as faults will be introduced in the simulations and an analysis of the ability to detect them from the character with image or video anomaly detection methods compared with traditional process monitoring techniques will be performed.

GenAI-Assisted Comprehensive Sustainability Assessment for Decarbonization of Chemical Manufacturing System

Faculty mentor: Dr. Y. Huang | Disciplines: Chemical Engineering, Computer Science, Industrial & Systems Engineering

Type II - Systems from product and process down to material design, molecular characterization, and atomic characterization

Improving stability and lifetime of organic semiconductor glasses using physical vapor deposition.

Faculty mentor: Dr. C. Bishop | Disciplines: Chemical Engineering, Mechanical Engineering

Thin films of organic semiconductors make up the active emitting layers of organic light emitting diode (OLED) displays, which are found in approximately half of all smartphone displays in the world. The active layers are prepared by physical vapor deposition (PVD), which results in uniform, defect-free glassy films that can be deposited at precise thicknesses without using toxic solvents. The lifetime and efficiency of OLEDs can be optimized through choice of proper physical vapor deposition conditions. Further, the components can be made to phase separate, extending their application to new materials such as organic photovoltaics. A quantitative relationship between deposition rate and temperature has been shown for molecular orientation in these device layers, while a similar principle remains to be investigated for phase separation and critical device parameters such as thermal stability and efficiency. In this project, participants will investigate the effects of deposition rate and substrate temperature on the phase separation, thermal stability, and lifetime of vapor-deposited organic semiconductor glasses. Students will learn to use an ultra-high vacuum deposition chamber to prepare the glasses and will then use various thin film characterization techniques and stability testing protocols to find the relationship between the deposition process and the properties of the resulting materials. Participants will identify which process parameters have the largest effect upon the stability, and how they can be balanced with one another to accommodate real-world concerns in manufacturing.

Computational design new materials for the efficient separation of Rare Earth Elements

Faculty mentor: Dr. J. Potoff | Disciplines: Chemical Engineering, Computer Science

Rare earth elements (REE) are comprised of the 15 lanthanides with atomic numbers 57-71 and are a critical component in advanced technologies, such as smart phones, wind turbines, catalysts, and batteries. Despite REE’s critical role in green technologies, such as electric cars and wind and solar power, the extraction of REE produces large amounts of toxic waste. While abundant in the Earth’s crust, REE exist in low concentrations in minerals. Therefore, large amounts of ore must be processed to obtain useful amounts of REE. In a typical REE extraction process, ore is dissolved in acid and collector ligands are used to extract REE from solution. Due to the chemical similarities, REE can be challenging to separate from each other, as well as more abundant metals, such as iron or nickel. The objective of this project is to the improve the sustainability of REE extraction and purification processes. This will be achieved through computational design of new materials for the efficient separation of REE. Students will use molecular dynamics simulations, combined with machine learning, to understand the interactions of rare earth elements with surfactants and/or collector ligands, such as dodecyl sulfate, or and metal organic frameworks (MOF), which have shown promise for REE separation. Computation screening will be performed on hundreds of thousands of MOFs to identify those with the greatest potential for selective separation of REE. Calculations will be fully automated using Python workflows that integrate software such as signac and the Molecular Simulation Design Framework (MoSDeF). MoSDeF significantly lowers barriers to learning how to perform complex molecular dynamics simulations and subsequent analysis.

Designing interfaces for wide-temperature Li-ion batteries

Faculty mentor: Dr. L. Arava | Disciplines: Chemical Engineering, Mechanical Engineering

Designing interfaces for wide-temperature Li-ion batteries. Li-ion battery applications are rapidly growing, including consumer electronics, military equipment, electric vehicles, and stationary storage applications. Conventionally, organic liquid electrolytes in current Li-ion batteries will deliver stable performance only between room temperature to 60°C, severe capacity degradation is observed for storage and/or cycling at high temperatures. To date, several research groups have investigated the high temperature compatibility of Li-ion rechargeable batteries using functional electrolyte additives, solvent engineering, and electrolyte design strategies. However, successful implementation of rechargeable Li-ion battery chemistry at high temperature operation is thwarted by many technical bottlenecks such as thermal stability and interfacial stability of the electrode and electrolyte materials. Fundamentally, electrode-electrolyte interface is the major source of degradation. Thus, gaining a comprehensive understanding of the surface and bulk structural, as well as interfacial properties of Li-ion battery electrode-electrolyte interfaces across a wide temperature range and advancing high-temperature operation could pave the way for a new paradigm in successfully implementing wide temperature rechargeable Li-ion batteries. The main objectives of the project are: (i) design thermally stable ionic liquid solvents to formulate wide temperature range electrolytes for Li-ion batteries; (ii) develop high temperature stable electrolyte additives for stabilizing electrode-electrolyte interface; and (iii) translating the fundamental knowledge to practical cell configurations similar to cylindrical and pouch cells. Successful completion of the project is expected to develop sustainable, low-cost, high-energy, and wide-temperature Li-ion batteries to extend operation capability.

Rapid and scalable nanomanufacturing process of functional nanofiber materials

Faculty mentor: Dr. E. Zhu | Disciplines: Chemical Engineering, Mechanical Engineering

Rapid and scalable nanomanufacturing process of functional nanofiber materials. Luminescent nanofibers are emerging popularly because of potential applications in flexible displays, smart wearable fabrics, fluorescent printing, and etc20.21. However, incorporating luminescent materials, such as quantum dots (QDs) and inorganic fluorophores, in polymeric fibers remains practically challenging22-24. Here, we will investigate a facile and rapid ac-electrokinetic process to fabricate luminescent flexible, ultralong fibers through multi-component complex coacervates of polyethylene (PEG) capped CdTe QDs and polyelectrolyate, where the complex viscoelasticity can be modified by compositions to enable the nanofiber ac-electrospinning combined with microfluidic solution mixing. Polymer-QD composite fibers of diameter will be varied by electrospun jets with regulating the applied ac voltage and frequency. This study will showcase the exploitation of multicomponent polymer coacervates to assimilate highly photostable QDs in flexible polymeric fibers for broad biomedical and nanotechnological applications. REU students will participate in fundamental research to develop an integrated electrospray and microfluidic process to conduct ac-electrokinetic assisted supramolecular assembly of inorganic nanocolloids and polymers and control their hierarchical nanocolloidal assembly by ac-electrical field parameters. Specific tasks are: (1) fabricating microfluidic devices to control the mixing and concentration of QDs and Polymers for liquid-liquid separating coacervate formation, (2) directing the assembly of QDs and polymers into uniform and structured nanofibers by ac-electrospinning, and (3) in-situ characterizing the concentration and assembled structure of QDs and polymers in luminescent nanofibers.