
NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
Recipient: |
|
Initial Amendment Date: | August 20, 2019 |
Latest Amendment Date: | August 20, 2019 |
Award Number: | 1920363 |
Award Instrument: | Standard Grant |
Program Manager: |
Joanne Culbertson
CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
Start Date: | September 1, 2019 |
End Date: | August 31, 2020 (Estimated) |
Total Intended Award Amount: | $326,960.00 |
Total Awarded Amount to Date: | $326,960.00 |
Funds Obligated to Date: |
|
History of Investigator: |
|
Recipient Sponsored Research Office: |
210 N 4TH ST FL 4 SAN JOSE CA US 95112-5569 (408)924-1400 |
Sponsor Congressional District: |
|
Primary Place of Performance: |
One Washington Square San Jose CA US 95112-5569 |
Primary Place of
Performance Congressional District: |
|
Unique Entity Identifier (UEI): |
|
Parent UEI: |
|
NSF Program(s): | Major Research Instrumentation |
Primary Program Source: |
|
Program Reference Code(s): |
|
Program Element Code(s): |
|
Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.041 |
ABSTRACT
This Major Research Instrumentation (MRI) award provides funding to acquire a metal additive manufacturing (AM) system for research and education. The instrumentation will enable researchers to develop new knowledge on the production of metals with high fracture and fatigue resistance, which are needed for the biomedical, defense, aerospace, and automotive industries. The instrumentation will also catalyze research advances in novel on-demand design of metamaterials for acoustic cloaking, biomedical imaging, and health monitoring. The metal AM system will enrich graduate and undergraduate education, support outreach activities at K-12 schools, and aid teacher workshops. A new virtual reality metal AM teaching module will be developed and made available for engineering students, high-school students, and the public. Research findings will also provide the basis for a new machine learning and computational mechanics class.
The requested metal AM system will provide structural information on defects and pores that can be processed at the micro- and meso-scale, providing insights into the processing limits of the SLM system. A new SPH model will be developed to understand the powder-level physics during the SLM process; thus, advancing fundamental understanding of the structure development during the SLM of hierarchical metals. Neutron diffraction will be used to reveal the processing/structure effects on the residual stresses in hierarchical SLMed metals. Mechanical behavior of the hierarchical metals will be observed via tensile and fracture toughness tests. Using this new processing-structure-property information, FEM simulations will be performed to discover the origins of hierarchical toughening mechanisms in SLMed metals.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
A new metal additive manufacturing system--EOS M100--was installed at the minority-serving San Jose State University. Due to COVID-19 outbreak, the equipment training was delayed, but various samples were successfully manufactured using Ti6Al4V alloy. The samples included: fatigue testing specimens, cylindrical samples for machining and structure investigations, and a pentamode metamaterial. These specimens initiated the research towards the discovery of laser-metal powder-structure interrelationships in hierarchical metals, investigations on the selective-laser-melted-metal surface structure and machining relationships, and processability of metamaterials. Through various collaborations, the origins of the defect creation during the laser and metal powder interactions are being investigated. A new fully convolutional neural network was created to correlate porous structure to stress distributions, which will be extended to include plastic deformation in hierarchical metals with defects. From a broader impacts point of view, three different additive manufacturing (3D printing) courses will benefit the new metal AM modules that were created to teach selective laser melting process. A new course "MATE 244 - Introduction to Materials Informatics and Data Sciences" was developed, which will include modules on machine learning approaches in metal AM processing-structure-property interrelationships. These courses will impact approximately 150 students per year and help develop the next generation manufacturing workforce. In addition, a new virtual learning environment is being developed to facilitate learning of powder-bed laser metal additive manufacturing in virtual reality.
Last Modified: 11/28/2020
Modified by: Ozgur Keles
Please report errors in award information by writing to: awardsearch@nsf.gov.