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Results for “fused deposition modeling (FDM)”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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21 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Structure-property relationship between lignin structures and properties of 3D-printed lignin composites

Lignin is a low-cost and renewable bioresource with a huge annual production promising to prepare sustainable materials. However, the poor interfacial adhesion between many lignin-polymer pairs deteriorates the mechanical performance of the composites, which seriously limits the application of lignin in 3D printing via fused depositional modeling. This work examined lignin-polyamide 12 (PA 12) intermolecular interactions (e.g., hydrogen bonding) to address the interface challenge. To realize this goal, the phenolic hydroxyl content was increased for a kraft softwood lignin using a LiBr/HBr demethylation procedure, increasing phenoxy content by 61.7%. Increased hydrogen bonding interactions between modified lignin (Pine-Lig-OH) and PA 12 demonstrated a significantly improved molten dynamic modulus by rheological analysis. Regarding mechanical properties, by adding 20 wt% of Pine-Lig-OH, the tensile strength and Young's modulus reached 46.6 MPa and 1.62 GPa, 30.2% and 33.9% higher than PA 12, respectively. Further morphological analysis proved the interfacial interactions are enhanced by showing the difference in the phase gaps. The dynamic mechanical analysis (DMA) supported the conclusion that Pine-Lig-OH could interact with polymer chains, alternating segmental movements due to the strong interaction. Here, this study presents a method to enhance lignin composite properties by promoting interactions with the polymer matrix through modified functional groups, guiding future lignin composite research.

36 MATERIALS SCIENCE↗
DOE OSTI2020

In-situ X-ray scattering study of isotactic polypropylene/graphene nanocomposites under shear during fused deposition modeling 3D printing

Complementary techniques were applied to characterize the structure property relationships of FDM printed isotactic polypropylene/graphene nanocomposites. Raman spectroscopy indicated that graphene was mostly exfoliated during the nozzle extrusion process. Rheological measurements of the viscosity indicated that the presence of graphene induced shear thinning during extrusion. Azimuthal scans of wide angle x-ray scattering indicate correlated thermal fluctuations of the graphene platelets which stops abruptly at the onset of transcrystallization, or templating of the iPP chains on the graphene. X-ray microbeam SAXS analysis show the formation of ‘shish-kebab’ structures at the core of the unfilled filaments, where the ‘kebab’ component vanishes at the fiber-fiber interface enabling interdiffusion. Weak lamellar structure, and a corresponding decrease in the storage modulus are observed with the addition of 5% graphene. The lamellar structure disappears entirely with the addition of 10% graphene, while the storage modulus is decreased by nearly 50%. Thermal imaging of heat flux dissipation across molded and printed samples shows no orientational effects in pure iPP despite differences in crystal structure. Thermal conductivity increases dramatically in the samples with 10% graphene, and strong orientational effects are observed where the thermal coefficients differ by 180% between samples printed parallel or perpendicular to the heat flux.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Advancing Industry 4.0: Multimodal Sensor Fusion for AI-Based Fault Detection in 3D Printing

Additive manufacturing, particularly fused deposition modeling, is transforming modern production by enabling rapid prototyping and complex part fabrication. However, its layer-by-layer process remains vulnerable to faults such as nozzle clogging, filament runout, and layer misalignment, which compromise print quality and reliability. Traditional inspection methods are costly, time-intensive, and often limited to post-process analysis, making them unsuitable for real-time intervention. In this current study, the authors developed a novel, low-cost, and portable faultdetection system that leverages multimodal sensor fusion and artificial intelligence for real-time monitoring in FDM-based 3D printing. The system integrates acoustic, vibration, and thermal sensing into a non-intrusive architecture, capturing complementary data streams that reflect both mechanical and process-related anomalies. Acoustic and thermal sensors operate in a fully contactless manner, while the vibration sensor requires minimal attachment such that it will not interfere with printer hardware, thereby preserving portability and ease of deployment. The multimodal signals are processed into spectrograms and time-frequency features, which are classified using convolutional neural networks for intelligent fault detection. The proposed system advances Industry 4.0 objectives by offering an affordable, scalable, and practical monitoring solution that improves faultdetection accuracy, reduces waste, and supports sustainable, adaptive manufacturing.

42 ENGINEERING↗