04/29/2024
By Danielle Fretwell

The Francis College of Engineering, Department of Mechanical Engineering, invites you to attend a Doctoral Dissertation Proposal defense by Kyeongbin (Ryan) Min on "High-Throughout Design and Analysis of Novel Ceramics."

Candidate Name: Kyeongbin (Ryan) Min
Degree: Doctoral
Defense Date: Monday, May, 13, 2024
Time: 12:30 to 2:30 p.m.
Location: Southwick 240

Committee:

  • Advisor: Christopher J. Hansen, Professor/Chair, Mechanical and Industrial Engineering, UMass Lowell
  • Amy Peterson, Associate Professor/Associate Chair-Master's Studies, Plastics Engineering, UMass Lowell
  • Alireza Vakil Amirkhizi, Associate Professor, Mechanical and Industrial Engineering, UMass Lowell
  • Lei Chen, Assistant Professor, Mechanical and Industrial Engineering, UMass Lowell

Brief Abstract:
This study aims to investigate high-temperature ceramics fabricated using additive manufacturing techniques, particularly in dynamic impact applications. There are three scientific aims of the proposed work: (1) To investigate how parameters like particle composition, size distribution, and volume fraction influence grain size distribution of a ceramic microstructure; (2) To investigate the viscosity caused by the adding binder influence the making pores, cracks, or technical defects in the ceramic microstructure; (3) To create a microstructure optimized for mechanical properties by ballistic or impact test. Our approach involves selecting materials that exhibit the desired properties and formulating a composite with suitable viscosity for extrusion through a 3D printer while maintaining its shape post-extrusion. We verify the extrudability and shape retention of the composite material. The printable material is then extruded using a 3D printer with a designed G-code to produce green body specimens that will be sintered and polished to observe the aggregation of ceramic particles. Furthermore, we intend to evaluate the material's performance as protective equipment through ballistic testing. This data will be provided to collaborators for a machine learning-enabled materials discovery process.