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Community College Research 2019

 

NCN URE Project Descriptions

Fabrication of particulate products: a computational approach to manufacturing (This project is appropriate for lower division and community college students)

Faculty Advisor: Marcial Gonzalez

Marcial Gonzalez

Professor Marcial Gonzalez
Mechanical Engineering

Project Description: Particulate products are ubiquitous and highly valued across a range of industry sectors, as diverse as agricultural, energetic materials and pharmaceutical. These products contribute more than one trillion dollars to the U.S. economy, which is the world’s larger manufacturer in this sector. Compaction of micro- and nano-powders is a manufacturing process used in most of these industries. It consists of the synthesis of loose powder blends into solid bodies. Since the performance of particulate products is directly related to their microstructural features, the fundamental understanding of the compaction process becomes of paramount importance. The first goal of the project is to expand the current capabilities of the nanoHUB tool Powder Compaction (https://nanohub.org/resources/gscompaction) by: (i) modeling compacted products of different shapes and sizes, (ii) accounting for the elastic relaxation compacted powders experience after being plastically deformed during fabrication. These two features are key to have a realistic computational approach to the manufacturing process, and thus address questions relevant to manufacturability and product quality. The second goal of the project is to calibrate and validate the predictions of the nanoHUB tool using experimental data obtained with a bench top tablet press.



Image: Fabrication of particulate products: a computational approach to manufacturing.

Suggested Majors:

  • Mechanical Engineering
  • Civil Engineering
  • Materials Science Engineering
  • Chemical Engineering
  • Computer Science

Required Skills:

  • Basic understanding of mechanical properties of solid materials:
    • Roy R. Craig Jr., Mechanics of Materials, Chapter 2 -- especially the parts on stress-strain curves
    • The same content is also in many MSE textbooks, such as Callister
    • There is also a lecture on nanoHUB that covers the content at a more advanced level: https://nanohub.org/resources/6052
  • Basic coding experience in MATLAB and willingness to learn

Additional Desired Skills: 

  • Completion of any science/engineering course that had a laboratory component
  • Familiarity with the current research activities taking place at Purdue’ Center for Particulate Products and Processes (CP3): https://engineering.purdue.edu/CP3

Deep learning based material by design (This project is appropriate for lower division and community college students)

Faculty Advisor: Guang Lin

Guang Lin

Professor Guang Lin
Mechanical Engineering and Mathematics

Project Description: In the research field of engineering, material design plays a crucial role. During the summer study, the students will investigate a number of deep machine learning algorithms to learn how to build effective surrogate models to predict the material performance given the datasets. Then apply the constructed deep-learning based surrogate model to do material design. We expect to develop a deep-learning based software to design the optimal material given the desired material performance requirement. The goal is (1) Develop a deep-learning based model to predict material performance; (2) develop a deep-learning based software to design the optimal material. This work has wide different applications in material manufacturing and design.

This project includes both theory and implementation in machine learning software development. The student will learn the concepts and applications of deep learning, optimization, and material design .

Suggested Majors:

  • Engineering
  • Computer Science
  • Mathematics and Statistics

Required Skills:

  • The student working on this project will be expected to have finished one semester of calculus and one programming course.

Additional Desired Skills: 

  • The student should be familiar with Python and some deep learning modules, such as Tensorflow, and familiar with hyper-parameter tuning to build a regression model that maps the inputs to the outputs.
 

Phase transforming cellular materials (This project is appropriate for lower division and community college students)

Faculty Advisor: Pablo D. Zavattieri

Pablo D. Zavattieri

Professor Pablo D. Zavattieri
Civil Engineering

Project Description: Phase transforming cellular materials (PXCMs) are a family of novel architectured metamaterials which can dissipate energy elastically in response to large reversible strains. This new type of materials could be utilized in many applications including, but not limited to, buildings, automobiles, and protective gear. The PXCMs building blocks consist of a snapping mechanism that can be designed to have multiple stable configurations. Each stable configuration is associated with a unique material phase. Under large strains, the progressive phase transformation of each unit cell in a PXCM results in energy dissipation.

Based on our initial design, fabrication and numerical analysis, we developed a simulation tool on nanoHub that can predict the mechanical behavior of PXCMs based on simple geometrical parameters and material properties. Now we would like to extend this nanoHUB tool (or create a new one) to predict the behavior of PXCMs under external stimuli such as temperature, moisture, and/or electric current. Our goal is to create a simulation tool that can design and predict the response of the PXCMs under these stimuli, and ultimately predict temperature-induced and stress-induced phase transformation.

Suggested Majors:

  • Computer Science
  • Mathematics
  • Mechanical Engineering
  • Aerospace and Aeronautical Engineering
  • Civil Engineering

Required Skills:

  • Programming in MATLAB, Python, or C
  • Knowledge of mechanics of materials and structures