In-Situ Monitoring for Quality Assurance and Machine Learning in Direct-Write Additive Manufacturing of 5G RF Electronic Ceramics
Abstract
Next-generation RF materials and advanced manufacturing are central to the success of several Naval priority areas and research objectives. Specifically, while the reduced latency, higher rate data telemetry, and security of 5G/FutureG RF will significantly enable the combination of communications and surveillance electronic warfare and is also central to enabling autonomy in various Navy systems, additive manufacturing (AM) methods provide pathways to agile, efficient, and customizable advanced manufacturing. However, the microstructure, properties, and performance of AM materials cannot presently be assured with the same certainty as their traditionally manufactured equivalents. This knowledge gap underpins a significant technology bottleneck and serves as the justification for the proposed research. The overall goal of the PI is to engineer bulk performance (i.e., obtained in traditionally made materials) in additively manufactured materials for next-generation 5G/FutureG RF applications. The approach 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 produced by 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 the printing of RF electronic ceramics. These studies will determine how the slurry chemistry, morphology and rheological properties together with thermal treatments drive the basic chemical and physical mechanism(s) that govern pressureless densification, sintering, and coarsening in builds 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 interfaces and can have outsized effects on properties. Therefore, the structure and chemistry of grain boundaries are integral to these studies.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.4. Identify the mechanism of bonding and adhesion at direct-write metal (Ag and Cu)/ electronic ceramic interfaces.5. Investigate integration of aspects of traditional electronic materials processing to enhance the properties and performance of direct-write AM materials.6. Adopt processing models based on multi-physics, three-dimensional, computational thermal modeling to simulate and obtain deeper insight into the thermokinetics 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 will be achieved. This science-based toolset can be applied to any AM method that facilitates the integration of in-situ monitoring and diagnostic probes, and is expected to provide a broad, transformative capability for AM of materials with designed structure, properties, andperformance.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 21, 2023
- Source ID
- N000142412010
Entities
People
- Nigel Shepherd
Organizations
- Office of Naval Research
- United States Navy
- University of North Texas