Restricted Research - Award List, Note/Discussion Page
Fiscal Year: 2023
87 University of North Texas (141975)
Principal Investigator: Shepherd,Nigel Dexter
Total Amount of Contract, Award, or Gift (Annual before 2011): $ 447,767
Exceeds $250,000 (Is it flagged?): Yes
Start and End Dates: 9/1/22 - 8/31/25
Restricted Research: YES
Academic Discipline: Materials Science & Engineer
Department, Center, School, or Institute: College of Engineering
Title of Contract, Award, or Gift: In-Situ Monitoring for Quality Assurance and Machine Learning in Direct-Write Additive Manufacturing of 5G RF Electronic Ceramics
Name of Granting or Contracting Agency/Entity:
U.S. Office of Naval Research
CFDA Link: DOD
12.300
Program Title:
none
CFDA Linked: Basic and Applied Scientific Research
Note:
Although a central necessity of additively manufactured (AM) materials is that they must satisfy performance requirements, the microstructure, properties, and performance of AM materials cannot presently be assured with the same certainty as their traditionally manufactured equivalents. Further, as it relates to advanced RF generally, while 5G is currently being deployed in the lower 4G adjacent frequency ranges, significant and numerous challenges remain for the higher frequency bands, beginning with materials for the various network components. The principal near-term objective of the PI is to use in-situ monitoring to define the processing rules that determine the microstructure, macrostructure, and high-frequency dielectric response of electronic ceramic materials for advanced RF "grown" by laser-assisted direct-write AM. The specific aims of this research are to: 1. Optimize implementation of in-situ monitoring by Raman and IR spectroscopies in a customized direct-write AM platform for the measurement of temperature, phase, structure, and stress during printing of RF electronic ceramics. These studies will determine how particle size and distribution, shape, slurry chemical composition, and slurry rheological properties together with the direct-write parameters and heat from 1.06 micrometre laser irradiation influence the basic chemical and physical mechanism(s) that govern sintering, densification, crystallization, and domain growth in films of electronic ceramics. 2. Establish ex-situ validation using post-manufacture structure and properties characterization to correlate and verify in-situ determinations. Phase precipitation and segregation as well as impurity and defect segregation often occur at boundaries and can have outsized effects on properties. Therefore, an emphasis of this study is understanding grain size and porosity, and the structure and chemistry of grain boundaries. 3. Determine the properties correlation with the aforementioned processing and resultant structure, including defect and interfacial structure, and the fundamental mechanisms of dipole formation, polarization, and dielectric response in the 5-40 GHz range. Crystallinity, defects, the existence of permanent dipoles, and the mobility of free carriers all contribute to the dielectric response. 4. Adopt processing models based on multi-physics, three-dimensional, computational thermal modeling to simulate and obtain deeper insight into the thermokinetics (thermodynamics and kinetics) of laser-matter interactions in the AM process. In-situ measurements will be correlated and validated with ex-situ characterization, and these data will iteratively guide synthesis and processing optimization. In parallel, the experimental measurements will be used as inputs for the physics-based process modeling. The datasets generated by in-situ monitoring and ex-situ verification are limited by experimental measurements, whereas the datasets generated by the models will be large and governed by computing resources. The computations and modeling, therefore, result in the amplification of experimentally validated data. Predictions from the experimentally validated models will in turn: 1) guide synthesis and processing optimization and, 2) train and constrain ML. Thus, quality assurance is achieved. This science-based toolset can be applied to any laser-assisted AM method that facilitates the integration of in-situ monitoring and diagnostic probes, and is expected to provide a broad, transformative capability for customizable, agile, affordable, high-throughput additive manufacturing of materials with designed structure, properties, and performance. In the longer-term, the new knowledge, methodology and data generated will be the foundation for training of ML models.
Discussion: No discussion notes