Summer Undergraduate Research in Engineering (SURE)

SURE offers summer research internships to outstanding current U-M undergraduate students who have completed their sophomore or junior year (preference will be given to those who have completed three years of study) by the time of their internship.  Participants have the opportunity to conduct 10-12 weeks of full-time summer research with some of the country’s leading faculty in a wide range of engineering disciplines. The program provides opportunities for students to assess their interests and potential in pursuing research at the Masters or Ph.D. level in graduate school.  All participants must apply online through the SURE website.  Accepted applicants from the University of Michigan receive guidance by a faculty advisor in a College of Engineering research facility, a stipend of $5,000, attend regular meetings and seminars and contribute to an abstract booklet with highlights of their summer research project and/or experience.

Learn more:

~Watch this space for SURE 2021 Research Projects in December 2020~


Mechanical Engineering 2020 SURE Research Projects


ME Project #1: 3D-Printing of Custom Assistive Devices
Faculty Mentor: Albert Shih,
Project Description: This project explores the use of material extrusion (MEX), one of the additive manufacturing processes, for 3D-printing of various assistive devices. The project works closely with the University of Michigan Orthotics and Prosthetics Center with the goal to develop design and manufacturing methodologies for a new service system for rapid turn-around and high-quality 3D-printing of assistive devices that will have personalized fit and comfort. Such assistive devices including lower and upper limb prosthesis, orthoses for diabetes partial foot amputees, ankle foot orthoses for stroke patients. Contact modeling based on the computed tomography (CT) or 3D scanning images will be developed to design the geometry.

ME Project #2: Miniature Wireless Data Acquisition Devices for Spatial-Temporal Mapping of Sound
Faculty Mentor: Bogdan Popa,
Project Description: The goal of this project is to design, fabricate and validate a miniature wireless data acquisition system that will measure and track sound propagating in a given space during a given time interval. The qualified student will collaborate with the faculty advisor and PhD students to build a device consisting of micro-electromechanical microphones connected to a Bluetooth module and a Teensy microcontroller — a much more powerful and smaller Arduino-like device. The data acquisition system will produce movies of how sound propagates in a given space and will visually inform on how the sound interacts with the objects it encounters. The qualified student will get hands on experience on designing and fabricating professional printed circuit boards (PCBs) and will learn how to interface them with microcontrollers to obtain full fledged embedded systems. Last but not least, the student will get new understanding of wave phenomena.

ME Project #3: Engineering a Synthetic Neuron Using Bottom-Up Synthetic Biology
Faculty Mentor: 
Allen Liu,
Project Description: Building synthetic cells is an exciting area of synthetic biology with opportunities to unravel basic design and organizational principles of cellular life. Besides a cellular reconstitution approach of using purified protein components, the current paradigm in the construction of a bottom-up synthetic cell system involves lipid bilayer vesicle encapsulation of cell-free expression systems to engineer active membranes. The Liu lab and his collaborative team have identified neuron as a potentially tractable system to construct from the bottom-up. Neurons convert biochemical information (through binding of a neurotransmitter) to electrical signal (via action potential) and back to biochemical signal (through the release of neurotransmitters). These distinct and separable processes can be reconstituted in a synthetic neuron by using natural and engineered proteins. As part of the team, the student involved will be offered an opportunity to work on reconstituting ion channels using a mammalian cell-free expression system and testing their functions using electrophysiology. Candidate with interests in biology and hands-on lab work is highly desirable. Basic knowledge in chemistry and molecular biology is a plus.

ME Project #4: Computational Studies of Bubble Collapse
Faculty Mentor: Eric Johnsen,
Project Description: The goal of this project is to computationally investigate the potential damage produced by collapsing cavitation bubbles. This research is motivated by cavitation erosion observed in naval hydrodynamics (e.g., propellers) as well as deliberate damage produced by ultrasound-induced cavitation to destroy pathogenic tissue. The student will use an in-house computational code to conduct the simulations of interest.

ME Project #5: Flexure Mechanism based High Performance Motion Stages for Semiconductor Inspection and Metrology
Faculty Mentor: Shorya Awtar,
Project Prerequisites: Candidates should meet the following prerequisites: (1) Background in Mechatronics covering practical aspects of modeling mechanical and electrical systems, control theory, sensors & actuators, (2) Proficiency in engineering design and analysis tools such as SolidWorks, MATLAB, and (3) Experience in machining, fabrication and assembly of mechanical components and systems.
Project Description: The aim of this project is to design, build and demonstrate unprecedented motion performance at an economically viable price point for semiconductor wafer metrology using flexure mechanism based motion stages. Students will carry out one or more of the following activities: (1) Conducting literature review to understand state of the art Flexure mechanism based motion stages, (2) Carrying out the static and dynamic analysis of proposed flexure mechanisms using FEA based tools and/or other analytical methods such as Lagrangian dynamics (3) Actuator and Sensor selection, (4) Driver and Power Supply characterization for the selected actuators, and (5) Developing CAD for the proposed proof of concept design. The ideal candidates should be hardworking and be willing to put around 15-20 hours of work per week, have a strong technical background in mechanical engineering. and be eager to learn Mechatronics.

ME Project #6: Design of Novel Upper Limb Prosthesis
Faculty Mentor: Shorya Awtar,
Project Prerequisites: Candidates should be proficient in SolidWorks and MATLAB and have experience in machining, fabrication and assembly of mechanical components and systems.
Project Description: The mechanical design of powered upper limb prostheses has been an area of intense research for the past 40 years. However, there is still a significant gap in the performance of these prostheses when compared with the capabilities of a human arm. The aim of this project is to design and build a novel upper limb prosthesis that seeks to fill in the performance gaps that exist in current prostheses. Students will be working in (1) Static and Dynamic modeling of elements of the prosthesis, (2) Research and modeling of different types of actuators, and (3) Design and fabrication of elements of the prosthesis. The ideal candidate should be passionate about learning more about Mechatronics system design and prostheses in general.

ME Project #7: Preemptive Intervention Strategies to Mitigate Motion Sickness in Autonomous Vehicles
Faculty Mentor: Shorya Awtar,
Project Prerequisites: The candidate should have a working knowledge of Simulink, SolidWorks, and Kinematics & Dynamics.
Project Description: The goal of this project is to mitigate motion sickness in the passengers of autonomous vehicles using preemptive corrective action. The student will assist the project by (1) conducting a survey of the state of the art, and analyze gaps in the field, (2) designing a model of an autonomous car that will be used for simulations to verify the performance of our system, and (3) developing CAD for concepts, and prototyping mock-ups using 3D printing, etc. Students will work alongside PhD students in the lab. The ideal candidate will have a strong technical background in mechanical engineering and a keen interest in mechatronic systems design.

ME Project #8: Automatic Fault Detection for 3D Printing
Faculty Mentor: Chinedum Okwudire,
Project Description: The student working on this project will help develop an algorithm for automatic detection of failures and faults in 3D printing using low-cost sensors. The ideal student should be very skilled in CAD software, MATLAB and C++ (or Python), and enjoy coding. Some familiarity with machine learning and 3D printing is a plus. The student will get to implement and test their algorithms on 3D printers.

ME Project #9:Active Metamaterials Toward the Design of Hybrid Energy Harvesting-Noise/Vibration Isolating Devices
Faculty Mentor: Serife Tol,
Project Description: Metamaterials can manipulate and control the propagation of acoustic/elastic waves in ways that are not possible in conventional materials. Although metamaterials have helped to advance technology, researchers have mostly relied on linear and passive unit cells which limit their applicability. This project focuses on the exploration of active unit cells with piezoelectric materials which can be controlled externally by means of electrical loading. The theoretical framework developed from this research will form the basis for the design of tunable and adaptive structures. The results of the project can be extended to novel concepts toward enabling next-generation multifunctional wave devices (i.e. energy harvesters, sensors, mechanical lenses, mechanical filters, noise and vibration absorbers, etc.). 

Keywords: Metamaterials, piezoelectric energy harvesting, vibration/noise isolation, active unit cell, elastic wave control, adaptive structures

ME Project #10Conformal Metamaterials for Preserving and Transmitting Wave Energy
Faculty Mentor: Serife Tol,
Project Description: This project focuses on conformal metamaterials/phononic crystals for non-planar structures. One direct application of this research is the design and integration of conformal elastic lenses with pipe-like structures to enhance sensing in nondestructive testing. Hence, signals carrying the critical damage information can be preserved and transmitted over long distances without suffering from the attenuation problem. Collaborative research explores the focusing of different wave modes in long range pipelines toward increasing the signal to noise ratio of the sensors and thereby improving damage detection capability. Fluid-structure interaction will also be investigated in this research. Additive manufacturing techniques are to be incorporated in the design and experimental phases of the project.

Keywords: Conformal metamaterials, wave focusing, enhanced sensing, nondestructive testing

ME Project #11Data-Driven Balance Training: Collection and Post-Processing of Older Adult Balance Data
Faculty Mentor: Kathleen Sienko,
Project Description: Age-related declines in balance function drastically impact quality of life and present long-term care challenges; falls are the leading cause of fatal and non-fatal injuries among older adults. Successful fall prevention programs include balance exercise regimes, designed to recover, retrain, or develop new sensorimotor strategies to facilitate functional mobility. We seek to develop an automated system for evaluating balance performance and recommending training exercises for older adults. Our approach leverages body kinematic data collected from inertial measurement units, and uses them to create data-driven models via machine learning techniques that can then accurately assess and predict patient performance. This SURE project will focus on the collection, post-processing, and preliminary analysis of older adult balance-related data in a controlled laboratory setting. The student will work together with a graduate student and/or a post-doctoral fellow to collect, post-process, and perform a preliminary analysis of inertial measurement unit, passive motion tracking, and video data.

ME Project #12: Data-Driven Balance Training: Data Analytics and Machine Learning
Faculty Mentor: Xun Huan,
Project Description: Age-related declines in balance function drastically impact quality of life and present long-term care challenges; falls are the leading cause of fatal and non-fatal injuries among older adults. Successful fall prevention programs include balance exercise regimes, designed to recover, retrain, or develop new sensorimotor strategies to facilitate functional mobility. We seek to develop an automated system for evaluating balance performance and recommending training exercises for older adults. Our approach leverages body kinematic data collected from inertial measurement units, and uses them to create data-driven models via machine learning techniques that can then accurately assess and predict patient performance. In particular, this SURE project will focus on the data analytics tasks. The student will work collaboratively within the team to process and manage collected datasets, explore and identify representative features, and build and validate machine learning models. Furthermore, the student will also explore the effects of uncertainty in the data and models, and analyze how it affects the prediction quantities. Experience in Python programming is a plus.

ME Project #13: Modeling Feedback Dynamics in the Arterial System
Faculty Mentor: Kenn Oldham,
Project Description: This project will compare experimental measurements of feedback mechanisms for regulating blood pressure in the cardiovascular system with models proposed from prior literature.  The faculty mentor’s research group has recently developed wearable sensing techniques to continuously measure changes in artery behavior, such as vascular resistance and stroke volume, that have generally been difficult to monitor non-invasively.  Preliminary results from animal and human subjects suggest that there are consistent feedback dynamics observed between blood pressure fluctuations and these phenomena.   The student will evaluate whether various feedback models presented in literature are consistent with apparent experimental feedback behavior, and attempt to fit model parameters to experimental subject data.   Suggest background of dynamic systems and fluid dynamics coursework.

ME Project #14One-way Waves
Faculty Mentor: Karl Grosh,
Project Description: In this research, new classes of highly directional acoustic waveguides are developed for transmitting acoustic waves in one direction either as part of a communication network or for absorbing waves for noise control. One goal is to produces an acoustic system were waves enter without reflection, a perfect acoustic black hole. We use mechatronic systems and modeling to achieve this goal. Our new approach is to use a network of distributed sensors whose signal is directed forward in space to an array of actuators located in a distributed fashion further down the waveguide. When the energy carried by an acoustic wave is fed forward in a unidirectional fashion via a distributed amplifier network, a preferential direction is created because the information (energy) is nearly instantaneously transmitted in only one direction by the electronics, while fluidic or structural disturbances propagate at the group velocity of the waveguide. This distributed, local control enables us to dramatically alter the wave propagation (we’ve essentially created an artificial wind to transport energy in one direction). We have had great success in working toward this goal and look to continue this through this summer SURE program. The summer research will involve electronics, acoustics, and vibration along with fabricating electro-mechanical systems.

ME Project #15: Connected Testbeds for Connected Automated Vehicles
Faculty Mentor: 
Tulga Ersal,
Project Description: Connected testbeds, i.e., remotely accessible testbeds integrated over a network in closed loop, will provide an affordable, repeatable, scalable, and high-fidelity solution for early cyber-physical evaluation of connected automated vehicle (CAV) technologies. Engineering testbeds are critical for empirical validation of new concepts and transitioning new theory to practice. However, the high cost of establishing new testbeds or scaling the existing ones up hinders their wide utilization. Enabling high-fidelity cyber-integration of existing but geographically dispersed testbeds can dramatically increase accessibility to engineering experimentation, just as the internet dramatically increased accessibility to information. This project aims to develop a scientific foundation to support this vision and demonstrate its utility for developing CAV technologies.

ME Project #16: Motion of Water Droplets on Lubricated Surfaces
Faculty Mentor: Solomon Adera,
Project Description: Controlling the motion of droplets on solid surfaces has broad technological implications ranging from microfluidics to thermal management. Past approaches utilized gradients in topography, temperature, chemical composition, surface tension or a combination thereof to manipulate droplets on solid surfaces. In this study, we utilize asymmetry of the wetting ridge to trigger motion of water droplets on lubricated surfaces. Specifically, we will investigate the motion (directionality and speed) of droplets on micro/nanoengineered surfaces. Additionally, we will investigate the criteria and time scale for coalescence. The results of this study will aid the rational design of lubricated surfaces for various engineering applications.

ME Project #17: Millimeter-size Bump Optimization for Fog/Water Harvesting
Faculty Mentor: Solomon Adera,
Project Description: Inspired by the Namib desert beetle, millimeter-size bumps have been demonstrated to enhance fog/water harvesting by directing vapor transport towards the apex of the bump. Experimental results have shown a 6x increase in water collection at the apex of a bump (due to higher vapor concentration gradient) when compared to the flat base. This work, which involves both experiment and modeling, is aimed at optimizing the size (diameter and spacing) of the bumps to maximize fog/water harvesting.

ME Project #18: Computational Study of Nanoparticle Agglomeration
Faculty Mentor: Angela Violi,
Project Description: Plan and carry out simulations to investigate aggregation of nanoparticles in various environments and the importance of chemical and physical characteristics of the nanomaterial on the mechanisms of growth. Duties will include running computations and simulations of structures searching for optimal mechanisms.  Experience in computer science/machine learning is beneficial.

ME Project #19:Developing a Simulation Analysis Toolkit in Python
Faculty Mentor: Angela Violi,
Project Description: Assist in the development of various post processing tools for molecular dynamics and Monte Carlo simulations. This project will expose you to many standard molecular techniques. Prior knowledge of python is required. Experience in computer science/machine learning is required.

ME Project #20:Computational Study of Biological Systems
Faculty Mentor: Angela Violi,
Project Description: Carry out and analyze simulations of mammalian cells and bacterial cells with and without nanoparticles. You will study how the presence of nanoparticles influence the assembly behavior of proteins, or how the permeation of membranes is influenced by nanoparticles presence. Prior knowledge of python is required, as well as experience in computer science/machine learning.