Undergraduate research projects cross-cut critical areas in sustainable manufacturing, with a focus on automotive manufacturing. Projects align with the following sustainable manufacturing areas: (1) Emerging and Environmentally Benign Materials and Manufacturing, (2) Sustainable Process Design and Control, and (3) Life-Cycle Engineering and Value Recovery (Circular Economy).

Emerging and Environmentally Benign Materials and Manufacturing

Sustainable Li-Ion Materials for Safe and Eco-Friendly Batteries (Mechanical Engineering, Chemical Engineering)

Current energy storage technologies such as batteries and supercapacitors fall short in both high power density and high energy density for portable electronics. Though several nanostructured materials have been proposed for enhancing energy density of supercapacitors, lack of cost-effective scalable manufacturing technologies held them away from mainstream applications. Hence, it is critical to develop a cost-effective and sustainable method for the synthesis of nanostructured electrode materials. In this project, L. Arava will guide REU students to explore the use of a special class of ionic solvents having liquid phase over wide temperatures range and graphene based hybrid nanostructures for the formulation of electrolytes and electrolyte of supercapacitors respectively. 

Sustainable Process to Concentrate Inorganic Nanoclusters and Polymers (Materials Science, Chemical Engineering)

Conventional production of self-assembled nanomaterials is highly time consuming and in poor control of structural uniformity, due to undesired and uncontrolled inter-particle interactions under particle thermal fluctuation. Advanced manufacturing approaches have utilized external fields, such as electric and optical fields, to overcome colloidal thermal fluctuation and direct the assembly of colloidal particles. However, scalable nanomanufacturing processes to rapidly and precisely assemble nanocolloids of 10 nm or smaller in size remain few and challenging. Under the supervision of Y. Zhu, REU students will participate in fundamental research to develop an integrated electrospray and microfluidic process to rapidly concentrate inorganic nanoclusters and polymers in droplet nanoreactors and control their hierarchical nanocolloidal assembly by dielectrophoresis.

Prediction of Environmental Fate Using Atom-Based Computer Simulation (Chemical Engineering, Materials Science)

Fluorotelomer-based compounds are used in a widespread array of consumer products, including coatings for cookware and stain resistant coatings for fabrics. These materials are now widely distributed in the ecosystem, with significant levels being found in the blood of wildlife and humans. Studies have shown that various fluorotelomer-based compounds can be oxidized in the environment to form perfluorocarboxylic acids [CF3(CF2)xCOO-] (PFCA) of which perfluorooctanoic acid (PFOA) and perfluoroctanoesulfonate (PFOS) are the most common. Many key parameters for the prediction of environmental fate have been estimated using Quantitative Structure Property Relationships (QSPR, but, the use of QSPR on PFCA is problematic. Under J. Potoff's supervision, REU students will build molecular models for surfactants from existing parameter libraries, and perform molecular dynamics and/or Monte Carlo simulations to calculate the physical properties of various surfactant molecules

Sustainability Assessment of Electroplating Systems and Technical Solution Identification (Chemical Engineering, Materials Science)

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.

Sustainable and Energy Efficient Computing Tehnology Manufacturing (Electrical and Computer Engineering)

Energy consumption of CMOS based computation, data storage and communication technologies are forecasted to surpass ∼ 1021 kWh in 2040, significantly exceeding today's global energy consumption of ∼ 1014 kWh. There is a strong need for next generation of computing devices to be sustainable and energy-efficient in order to reduce this significant energy demand. Brain-like (Neuromorphic) computing technologies offer a pathway to realize devices that can perform computations in a power efficient manner, mimicking the nature's most effective computers, our brains.  In this project, REU students will work with Dr. Tutuncuoglu and her PhD students, on simulation and characterization of nanoscale memristor devices that can mimic the brain synapses, and investigate additive manufacturing techniques for memristor fabrication.

Sustainable Process Design and Control

Evaluating Economic Model Predictive Control with Multi-Scale Models (Chemical Engineering, Industrial Engineering)

One way to enhance chemical manufacturing sustainability is through economic model predictive control (EMPC), which can optimize economic performance of the process through control actions while satisfying process constraints. Many simulation applications of EMPC for traditional chemical processes have been applied to standard chemical engineering models developed from mass and energy balances. However, the manner in which operating a process under EMPC may impact the dynamics of a process at a finer scale, and how models which incorporate smaller-scale phenomena, in addition to macroscopic behavior (multiscale models), would impact the optimal decisions of an EMPC. This project is suitable for one or two students working in H. Durand's lab. Students will explore capturing the dynamics of more complex processes with machine learning for use of the models capturing the spatio-temporal behavior in EMPC.

Data-Driven Modeling and Analysis of Energy Efficiency for Energy Sustainability Improvement in Manufacturing Zones (Chemical Engineering, Industrial Engineering)

Industrial energy use is about one-third of the total energy consumption in the U.S.  In manufacturing plants, significant energy loss occurs in process systems, energy generation, conversion, and distribution steps. Hence, how to improve energy efficiency has been a main research focus. In this project, an REU student will learn how to access and analyze publically available energy consumption and CO2 emission data in industrial zones that consist of various manufacturing sectors, and use a data-driven analysis method to study energy consumption and loss and its environmental impact in a selected manufacturing zone. In the study, the database from the U.S. Census Bureau and U.S. Energy Information Administration will be used, and a case study on the energy sustainability in an automotive manufacturing centered industrial zone will be conducted.

Modeling Degradation in Additive Manufacturing Fleets Subjected to Dynamic Loading Environments (Electrical Engineering, Industrial Engineering, Mechanical Engineering)

Effective operational strategies in modern manufacturing systems rely on an accurate understanding of energy usage patterns. This is particularly important for the next generation of Additive Manufacturing (AM) systems, where fleets of 3D printers can be coordinated to control production flow and energy usage by dynamically allocating operational load across manufacturing assets. The objective of this project is to develop a sensor-driven framework for predicting and controlling energy usage in AM fleets by leveraging real-time energy usage and production data. REU students will work in M. Yildirim's lab alongside PhD students on laboratory and analytical research tasks, focusing on conducting physical experiments with a fleet of 3D printers and identify AM fleet sensor data patterns.

Reliable Power Supply Systems for Electric and Autonomous Vehicles using Sustainable Energy Sources (Electrical Engineering)

As there have been more sensors, electronic components, circuits, and computing processors in the car, one of the core functions and requirements for future vehicles is to have a reliable power system. This REU project is developed to provide undergraduate students with cutting-edge research experiences in fields related to electric and autonomous vehicles, specifically on designing and manufacturing reliable power supply systems for electric and autonomous vehicles using sustainable energy sources. The students will gain the basic knowledge and research skills and be exposed to the ongoing research projects on power converters for electric vehicles and autonomous driving.

Future Manufacturing Automation: Translating Human Movements to Robotic Motions through Digital Twining (Industrial and Systems Engineering, Mechanical Engineering, Electrical Engineering)

Robots address a broad spectrum of roles in our everyday lives from factory automation and service applications to medical care and entertainment industry. Although originally designed for repetitive tasks, nowadays robots are involved in less structured and more advanced activities, where they are required to understand and even predict humans' intentions for successful interaction with people and completion of complex tasks. Under the supervision of S. Masoud, REU students will participate in fundamental research to understand the principles of human robot interactions, develop deep learning algorithms for prediction of humans' intentions, work with wearable technology, and to utilize physics-based modeling and mixed reality to develop realistic but safe interfaces for human robot interactions.

Life-Cycle Engineering and Value Recovery (Recycling, Remanufacturing, Reuse, etc.)

Characterizing and Measuring Error Propagation in the Digital Thread of Additive Remanufacturing using Laser Metal Deposition (Industrial Engineering, Mechanical Engineering)

Additive remanufacturing is particularly applicable for high value, metallic components, such as turbine blades, dies/molds, and engine blocks. Digital additive remanufacturing refers to the use of reverse engineering, CAD, and CAM (Computer Aided Manufacturing) technologies to digitize damaged areas using surface capture technologies such as laser line scanners, develop material deposition paths based on surface slicing algorithms and the digitized damage, and depositing material to rebuild damaged areas. The goal of this REU project will be to contribute to the study and characterization of how errors propagate in digital additive remanufacturing processes. REU students will work in J. Rickli's lab alongside Ph.D. students to develop error propagation models of surface damage characterization, covering the following steps; point cloud measurement, point cloud post processing methods, point cloud meshing, mesh slicing to create layers for material deposition, and fill/bonding of deposited material in damaged areas

Sustainable Life-Cycle Engineering Design 'App' to Cultivate a Community of Environmentally Sustainable Product Designers (Industrial Engineering, Electrical Engineering, Computer Science, , Mechanical Engineering)

Product manufacturers struggle with how to make sustainable design and manufacturing decisions and their sustainability goals. At the same time, the learning of sustainable engineering is facing difficulties in its integration into current college engineering curricula due in part to the cost of labs and time constraints. Constructionism is an educational approach for learning creative thinking by providing students with a collaborative environment for learning-by-making. K. Kim's lab has developed a digital platform (CooL:SLiCE) to provide a constructionist environment for learning sustainable product and manufacturing design. REU students will contribute to this research project by developing a sustainable engineering design 'app' to make the learning of sustainable engineering design available to industrial and academic learners to cultivate a community of environmentally sustainable product designers.

Energy-Aware Design of Automonous Robot Feet Management (Computer Science, Industrial Engineering)

Autonomous Robot (AR) fleets are rapidly increasing their market share and will soon find application in smart farms, private homes, retail stores, and autonomous security services. In particular, a multi-purpose AR allows the end user to flexibly decide the concurrent tasks to execute (e.g., navigation, face recognition, hazard detection) during a selected working period of several hours or even days. An aspect that will play an important role in the widespread adoption of multi-purpose AR fleets is the ease of management of the fleet for the end users. To this end, the objective of this project is to design and implement an Autonomous robots Fleet Manager (AFM) system that coordinates the task execution across ARs and their charging schedule on local charging stations to ensure continuity of operations while preserving battery lifespan. REU students will work in M. Brocanelli's lab alongside his PhD students in the Energy-aware Autonomous Systems Lab (EAS-Lab), focusing on the design of AR fleet management algorithms and on their implementation on a real physical testbed composed of several ARs.