2022 Research Projects

Projects are posted below; new projects will continue to be posted. To learn more about the type of research conducted by undergraduates, view the 2021 Research Symposium Abstracts (PDF) and search the past SURF projects.

This is a list of research projects that may have opportunities for undergraduate students. Please note that it is not a complete list of every SURF project. Undergraduates will discover other projects when talking directly to Purdue faculty.

You can browse all the projects on the list or view only projects in the following categories:


Fabrication and Robotics (15)

 

AAMP UP- Adhesion of Printed Energetic Materials  

Professor:
Stephen Beaudoin
Preferred major(s):
  • No Major Restriction
Desired experience:
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.

This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.

The project is run by Dr. Stephen Beaudoin and his team. Additively manufactured energetic materials do not adhere to themselves and casings with sufficient strength to survive gun launch. This project is focused on assessing the properties of the energetic composites that dictate how strongly the composites adhere to themselves and to their casings. The measurements will be made by cutting the composites and measuring the force required to initiate and propagate a crack, and also by using atomic force microscopy to measure directly the adhesion between energetic particles and binders and casings.

More information: https://engineering.purdue.edu/ChE/people/ptProfile?resource_id=11574

 

AAMP UP- Sample Heating using Infrared Laser and Optics 

Professor:
Wayne Chen
Preferred major(s):
  • No Major Restriction
Desired experience:
Must be a U.S. citizen, national, or permanent resident of the United States. Must have completed at least one academic semester of full-time study at associate's or bachelor's degree level from an accredited college or university.

This project is part of the AAMP-UP '22 program, which focuses on energetic material research.
AAMP-UP is separate but highly partnered with SURF.

The project is run by Dr. Wayne Chen and his team. Mechanical properties are important metrics that provide insight for different engineering applications ranging from chemical bonding type on an atomic scale to macroscale design applications. However, research shows that mechanical properties can change as a function of strain rate (impact velocity) and temperature. Therefore, it is necessary to test materials and gather properties while replicating the environment they will endure in application to best inform researchers and engineers in the material design process. A Kolsky bar apparatus is used to perform mechanical testing on materials at high strain rates. This experimental technique has been used for the last ~50 years and has resulted in many materials characterization papers. Missing from the literature is temperature dependence of mechanical properties at high strain rates. We would like a student interested in lasers and optics to design and build an infrared laser device that will evenly heat a polymer composite sample to a specified temperature. The device must attach to the Kolsky bar apparatus and be both safe and efficient. This will allow for coupled temperature and strain rate mechanical experiments and extrapolation of the temperature effects of different materials.

An understanding of laser and optics would be beneficial but is not required.

More information: https://engineering.purdue.edu/AAE/people/ptProfile?resource_id=1261

 

AAMP-UP: Additive Manufacturing 

Professor:
Jeff Rhoads
Preferred major(s):
  • No Major Restriction
Desired experience:
U.S. Citizenship Required Must have completed 1 semester of undergraduate courses

This research project seeks to additively manufacture (3D print) highly viscous materials using a novel 3D-printing method: Vibration Assisted Printing (VAP). This technique uses high frequency vibrations concentrated at the tip of the printing nozzle to enable flow of viscous materials at low pressures and temperatures. VAP has the potential to create next-generation munitions with more precision, customizability, and safety than traditional additive manufacturing methods. The objective of this project is to design formulations which are capable of being vibration-assisted printed, maintain energetic performance, and retain desirable mechanical properties after printing. The REU student would be mentored by graduate students and work within a team to design experiments, perform experiments, analyze data, and disseminate the results. The REU student will have the opportunity to present the findings in regular meetings, poster sessions, formal presentations, and papers.

More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=34218

 

Blood sample preparation for HIV diagnostics in a smartphone-based microfluidic device 

Professor:
Jacqueline Linnes
Preferred major(s):
  • Biomedical Engineering
  • Biochemistry
  • Biological Engineering - multiple concentrations
  • Microbiology
Desired experience:
3d printing and prototyping, medical technology

HIV/AIDS effects millions of people all over the world. The antiretroviral therapy used to treat HIV is effective, but HIV first must be diagnosed and then monitored to measure the treatment effectiveness to eliminate transmission to others and increase a patient’s quality of life. The Linnes Lab uses state of the art microfluidic technologies to prevent, detect, and understand the pathogenesis of diseases, such as HIV. This undergraduate summer research project will focus on developing new technology for HIV diagnostics that will also aid in diagnostics research of other bloodborne illnesses. The student will learn about biological sample preparation, nucleic acid amplification methods, microfluidic device design, fabrication, and testing, and rapid prototyping tools such as 3D printing and laser cutting. The researcher will develop a new tool for sample preparation of the blood that minimizes the number of user steps to integrate into an easy-to-use point-of-care diagnostic tool for people living with HIV to monitor their viral load within the convenience and privacy of their homes. The new tool design specifications include that it must be compatible with the smartphone imaging platform, microfluidic chip, and the HIV assay to diagnose the disease with high sensitivity and specificity.

More information: https://engineering.purdue.edu/LinnesLab

 

Design and scalable manufacturing of point-of-care diagnostics for infectious diseases 

Professor:
Jacqueline Linnes
Preferred major(s):
  • Engineering (First Year)
  • Automation and Systems Integration Engineering Technology
  • Biomedical Engineering
  • Electrical Engineering
  • Energy Engineering Technology
  • Engineering (First Year)
  • Industrial Engineering
  • Industrial Engineering Technology
  • Engineering / Technology Teacher Education
  • Materials Engineering
  • Mechanical Engineering
  • Mechanical Engineering Technology
  • Mechatronics Engineering Technology
  • Multidisciplinary Engineering
  • Robotics Engineering Technology
Desired experience:
knowledge or interest in programming, electronics design, manufacturing and automation techniques highly motivated to work in a highly cooperative, interdisciplinary, and productive translational research environment

Infectious diseases are a major cause of death and disability throughout the world. Research in the Linnes Lab focuses on using state of the art microfluidic and paperfluidic technologies to prevent, detect, and understand the pathogenesis of these disease. This undergraduate summer research project will focus on the and other infectious disease detection. The student will learn about robotic automation, screen printing of thin film devices, and other scalable manufacturing techniques and will develop strategies to reduce the number of manual interventions and errors during the fabrication process of paper-based diagnostic devices. The participant will also test and characterize different materials and explore design choices that do not interfere with the sensitivity of the test and do not compromise usability.

More information: https://engineering.purdue.edu/LinnesLab

 

Design of an IoT4Ag Robotic Sensor Deployment System 

Professor:
David Cappelleri
Preferred major(s):
  • Mechanical Engineering
  • Electrical Engineering
  • Computer Engineering
Desired experience:
US citizens/permanent residents only Mechanical design, mechatronics, 3D printing, electronics, robotics, programming experience preferred.

The goal of this project is to design an IoT4Ag sensor deployment system for autonomous agricultural ground robot. Two types of IoT sensors must be deployed by the robotic platform. Chaff sensors need to be distributed on the surface of soil at locations with designated spacing to ensure appropriate spatial coverage for the field of interest. The second type of sensors similarly need to be spread about the field but require them to be inserted into the soil at a depth of approximately 3” deep. Thus, the developed sensor deployment system should be able to 1. Store the sensors that need to be deployed; 2. Distribute sensors at a designated spacing above the soil; and 3. Insert the sensors into the ground at a designated spacing in the soil; and 4. Log the type of sensor that has been distributed, its sensor ID, and its placement location. This project will require the mechanical design of the deployment systems, mechatronic system design for operating and controlling the systems, and integration and interfacing with the agricultural ground robot for execution and tracking of sensor deployment locations. Field tests will be conducted at the Purdue University Agronomy Center for Research and Education (ACRE) facility.

More information: https://iot4ag.us/

 

Industrial IoT Implementation and Machine Learning for Smart Manufacturing 

Professor:
Martin Jun
Preferred major(s):
  • Mechanical Engineering
  • Computer Engineering
  • Computer Science

The student will work with PhD students on implementation of IoT technology on manufacturing machines and processes, database development, dashboard development, and machine learning for smart manufacturing.

More information: https://web.ics.purdue.edu/~jun25/

 

Mixed-Reality Testbed for Human-Robot Interaction 

Professor:
Mahsa Ghasemi
Preferred major(s):
  • No Major Restriction

In various emerging applications of autonomy, including autonomous driving, teleoperation, and assistive robotics, a human and an autonomous system closely interact with each other. This project’s goal is to develop a mixed-reality platform to facilitate research on trustworthy human-robot interaction. The platform will enable the creation of different environments and scenarios, in which human users/operators and robots interact, to facilitate the development and training of learning-based control algorithms for autonomous systems. The undergraduate researcher will contribute to setting up the mixed-reality platform as well as the design and implementation of high-level control algorithms.

More information: https://mahsaghasemi.github.io/

 

Modular soft robots 

Professor:
Adrian Buganza Tepole
Preferred major(s):
  • No Major Restriction
Desired experience:
Required: Electronics, controls (e.g. arduino or other micro-processors) Required: Programming Desired: Finite-element analysis

Soft robots offer new capabilities compared to traditional rigid robots due to their ability to continuously deform into arbitrary shapes and allowing safe interaction with humans. One limitation of soft robots is that they are not easily re-purposed. Modular robotics is a recent development to enable a wider range of application for soft robots. My lab has developed a type of modular soft robot that works much like Lego. Additional work is needed to refine the control systems and the mechanics of individual modules. A main motivation is to use the modular robots to build biologically-inspired assemblies.

More information: https://engineering.purdue.edu/tepolelab/

 

Multimaterial 3D Printing of Bioinspired Robotics 

Professor:
Alex Chortos
Preferred major(s):
  • No Major Restriction
Desired experience:
No specific experience is necessary. Any previous lab experience is an asset.

Technologies that integrate with biology enable new approaches to augmented reality as well as improved quality of life for people with medical conditions. To enable this integration, technology must take on some of the characteristic of biological systems, such as softness and 3D form factors. 3D printing can create soft electronic systems that mimic biological systems, including the ability sense their surroundings, process information, and actuate in response.
In this project, a student will work with a PhD student to prepare electronic materials, fabricate bio-inspired electronic devices and test their device operation.

There are different research scopes that are available depending on student interest/capabilities. Examples include:
-Materials development, consisting of preparing bio-inspired materials and optimizing their composition to achieve target electromechanical properties. Learned skills include elastomer chemistry, polymer physics, and electromechanical testing.
-Device fabrication, consisting of printing devices that include multiple electronic materials and testing their properties. Learned skills include device physics, printer operation and print path design, and circuit design for system measurement/controls.
-System modeling, consisting of modeling using COMSOL or ABAQUS to identify ideal device structures and materials properties that act as targets for experimental efforts. Learned skills include mechanical modeling software and application knowledge.

More information: https://engineering.purdue.edu/ME/People/ptProfile?resource_id=243743

 

Nanoscale High-Speed 3D Printing  

Professor:
Xianfan Xu
Preferred major(s):
  • Mechanical Engineering
  • Physics
  • Industrial Engineering
  • Computer Engineering
Desired experience:
Junior or Senior standing, knowledge in CAD, knowledge in Python is a plus

The ability to create 3D structures in the micro and nanoscale is important for many applications including electronics, microfluidics, and tissue engineering. This project deals with development and testing of a setup for building 3D structures using a femtosecond pulsed laser. A method known as two photon polymerization is typically used to fabricate such structures in which a polymer is exposed to a laser beam and at the point of the exposure the polymer changes its structure. Moving the laser in a predefined path helps in getting the desired shape, and the structures are then built in a layer by layer fashion. The setup incorporates all the steps from a designing a CAD model file to slicing the model in layers to generating the motion path of the laser needed for fabricating the structure. Like many other 3D printing processes, 3D printing at nanoscale is also slow. In order to make a 3D structure rapidly, many processes are currently being developed, including projecting 2D images and printing 3D structures in a rapid, layer-by-layer fashion. Other efforts include the use of machine learning to produce high quality 3D parts and printing materials other than polymers to achieve specific mechanical, electrical or optical properties. The undergraduate student will work with graduate student to learn the state-of-the-art 3D nanoprinting systems, help to develop rapid printing processes, and analyze printing results.

More information: https://engineering.purdue.edu/~xxu/; https://engineering.purdue.edu/NanoLab/

 

Solving Navigation Challenges for aerial and ground robots in agricultural fields 

Professor:
Dharmendra Saraswat
Preferred major(s):
  • Agricultural Engineering
  • Biological Engineering - multiple concentrations
  • Agricultural and Biological Engineering
  • Computer Science
  • Computer Engineering
  • Electrical Engineering
  • Industrial Engineering
  • Industrial Engineering Technology
  • Mechanical Engineering
  • Mechanical Engineering Technology
  • Mechatronics Engineering Technology
  • Robotics Engineering Technology
  • Automation and Systems Integration Engineering Technology
  • or related disciplines
Desired experience:
Desired Skills: · Python Programming · MATLAB Programming and Simulation · Robot Operating Systems (ROS) · TensorFlow / PyTorch / (equivalent machine learning framework) · Experience with robotics · Experience with circuit design · Interest in Unmanned Aerial Systems (UAS) · Highly motivated and ready to work in a team

The primary aim of this project is to explore mechanisms to establish reliable communication between aerial and ground robots. The aerial robot helps navigate a team of ground robots to reach objects of interest by avoiding collision with nearby objects in an agricultural field. The research would involve experiments with various deep learning models to identify objects of interest and develop an optimal path planning strategy, preferably using deep reinforcement learning. A Remote Control (RC) car platform is being used to mount sensors, edge devices, and implement learning algorithms. The edge device mounted on the RC car offers increased computational power to run the deep learning and deep reinforcement learning models in real-time. With an easy-to-use ROS interface, the RC car is ready to run various deep learning models

The research will be performed in two stages. The first stage would require developing a simulation environment of the field. In the second stage, the strategies designed for the simulation will be translated to RC car (hereafter referred to as ground robot) available in Digital Agriculture Discovery (DAD) lab.

In this project, one student will work with a Ph.D. student to help with the tasks identified below:

Student Task List:
· Report weekly progress using PowerPoint.
· Survey current literature - aerial guided navigation of ground robots and develop a report as per guidance provided
· Simulate aerial robots using a simulation environment
· Train deep learning-based object detection models for identifying objects of interest through aerial robots
· Establish communication between the ground and aerial robots using appropriate communication protocols
· Test the communication between the aerial and ground robots available in the lab
· Deploy deep learning models on the aerial robot
· Test deep learning models on the aerial robot for identifying objects
· Prepare a final report as per the format provided

 

Structural Engineering for Blast Resistant Design 

Professor:
Amit Varma
Preferred major(s):
  • No Major Restriction
  • Civil Engineering
  • Mechanical Engineering
  • Mechanical Engineering Technology
  • Aeronautical and Astronautical Engineering
  • Aeronautical Engineering Technology
  • Construction Engineering
  • Construction Management Technology
  • Engineering (First Year)
  • Materials Engineering
Desired experience:
Willing to work in a large-scale structural testing facility which may include some manual labor.

Today’s structures are highly engineered buildings and bridges capable of carrying everyday and extreme loads. In this project, students will get to work on understanding blast engineering design with a special focus on building materials like concrete and steel. Undergraduate researchers will work day-to-day alongside graduate students and permanent sta! to create test plans, fabricate test specimens, and test large-scale structures to failure. Students will leave this summer with a greater understanding of engineering principles including structural dynamics, impact and blast loading, and composite behavior.

More information: https://engineering.purdue.edu/~ahvarma/

 

Surface sound and AI based machine monitoring for smart manufacturing 

Professor:
Martin Jun
Preferred major(s):
  • Mechanical Engineering
  • Computer Engineering
  • Electrical Engineering
  • Computer and Information Technology
  • Computer Science

A stethoscope-based surface sound sensor has been developed at Purdue and this sensor is being used to monitor manufacturing equipment. Deep learning will be used to classify machine and process conditions using the sound sensor. Student will work with PhD student to implement sound sensors on machines in local manufacturing companies, collect data, build database and dashboard, and develop AI models.

More information: https://web.ics.purdue.edu/~jun25/

 

Understanding Soft Robot Growth 

Professor:
Laura Blumenschein
Preferred major(s):
  • No Major Restriction
Desired experience:
Basic physics Proficiency with data structure coding (excel, Matlab, etc.) Experience with optimization and fitting techniques is a plus but not necessary.

Soft growing robots are a new type of robot that move similar to plants: growing into their environments (vinerobots.org). While the mechanism of growth has been tested on a wide range of systems, from less that 1 mm to 10 cm in diameter and up to 97m long, the kinematics and mechanics behind this movement are not completely understood as of yet. This project builds on previous work collecting and analyzing data of robot growth using different materials and dimensions in order to build a model of the system. The student will build soft growing robots, design and run experiments to measure different properties of growing, analyze data gathered, and help build potential kinematic models while interfacing with other students working on growing robot projects. This work can help develop the basic equations that allow us and other researchers to understand how these robots move and what they can achieve.

More information: http://engineering.purdue.edu/raad