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SLAM takes an application-driven approach, across a wide array of requirements, to understand adoption areas for various material extrusion polymer-AM material systems and reduce material testing.
Only recently have appropriate process controls been applied to three-dimensional (3D) printers with production floor readiness capabilities in mind. Publicly available data is limited to test results provided by material providers or printer original equipment manufacturers (OEMs), often reporting averages of limited test sampling, and ignoring low-performing specimens or the overall spread of data. The lack of reliable data is a barrier to the translation of additively manufactured (AM) parts into a production setting, leaving production applications vulnerable even when used in a non-load, non-critical application space. Additionally, until recently, no standard criteria or specification of printing was available for creating a reliable and understandable data set.
The objective of this project was to deliver an open and comprehensive set of manufacturing tools to improve the rate of qualifying materials and part families. Another goal was to develop an application-driven framework to create material performance guidelines and service life predictive tools for manufactured components. The project sought to provide scalability from coupon level, combined with dataset interoperability, to further enable manufacturing technology implementations across relevant DoD services and their strategic/operational/tactical-level sustainment commands.
The technical approach included two phases. Phase 1 involved the creation of a rapid qualification methodology built around a full set of environmental conditioning with extensive dynamic characterizations performed on ULTEM 9085. RP+M’s AS9100-certified facility, used to generate the B-Basis Allowables for the previous UTLEM qualification, enabled the specimens and components to be analyzed without concern for unknown processing variables. Testing of the initial ULTEM 9085 specimens was conducted at the National Institute for Aviation Research (NIAR) and Auburn University. EWI’s nondestructive evaluation (NDE) techniques captured failure mechanisms and degradation progressions by sampling specimens at different aging stages and cycles. The results of the characterization and NDE methods created a probabilistic framework with the intent to estimate service life reduction and determine the risk of using AM for the proposed environmental conditions.
Phase 2 of the program continued with the selection of two additional materials from the provided prioritized list based on requirements gathered at the beginning of the program. The selected materials were Onyx FR, a composite-based carbon fiber-filled nylon, and ABS, a thermoplastic polymer. Optimized ULTEM 9085, Onyx FR, and ABS specimens and components were sent through a scoped characterization effort (aging and NDE). This provided an opportunity to quantify the process optimization efforts captured in the service life guide, validate the predictive service life model, and demonstrate the efficiencies gained by exercising the rapid qualification methodology. Specimens were tested after being fabricated and exposed to various environmental conditions in accordance with the standards outlined in the approved test matrix. The materials were exposed to elevated and cold temperatures, chemical corrosion, and solar exposure. After designated exposure, the materials were subjected to a designated static or fatigue test depending on the specimens’ respective geometries. Testing was performed using the setup requirements established per ASTM standards for each test type and geometry.
Testing and inspection of AM ULTEM 9085, Onyx FR, and ABS components were used to add entries to an existing service guide for materials in harsh environments. The project team effectively developed a predictive model that allows users to evaluate the expected performance of AM materials under a wide range of conditions. These conditions include baseline material properties, build orientation, and environmental conditioning. The model development utilized the data generated during this program including mechanical test data, NDE data provided by EWI, and fractography imaging. All the data was then input into artificial intelligence (AI) and machine learning (ML) software supplied and performed by MSC Software to train a predictive model for public use. The plastic sheet material identified in this report meets the Federal Railroad Administration (FRA) and NFPA 130 requirements as they pertain to surface flammability and rate of smoke generation.