Creating and curating new data appends the way we approach materials science. In polymer additive manufacturing (AM), the fabrication of parts and objects with high complexity and high performance is advantageous over other methods. Using nanostructured composites enables highly improved properties. With artificial intelligence and machine learning (AI/ML), formulation and manufacturing methods can be optimized. Using sensors capable of a feedback loop mechanism and the ability to use simulation to create digital twins, optimizing properties in record time is possible. Statistical and logic-derived design, including regression analysis, are starting points for designing experiments (DOE) or principal component analysis(PCA) in optimization and analysis vs trial-and-error approaches when working with polymer materials. In this talk, we demonstrate the approaches toward understanding Nanostructuring in composites and hierarchical approaches in optimization via AI/ML and other training/learning sets for specific properties and applications, such as 3D printing and flow chemistry reactions. Introducing more sensors (monitoring instruments) in AM processes and real-time ML with online monitoring allows a feedback loop and deep learning (DL) for autonomous fabrication and data analytics.

Additive manufacturing shows great potential in upgrading the production capabilities of many different materials, including energetic materials. Of specific interest is additive manufacturing’s applicability towards propellants. Although promising, there are fracture toughness concerns due to the welds created by the manufacturing process. Mechanical robustness is imperative if additive manufacturing is to become a common technique for production. The goal of this research is to develop the methods necessary to characterize the fracture behavior of additively manufactured energetics and apply these methods to 3D printed propellants. Y-shaped cutting is used to obtain materially intrinsic values of G, the strain energy release rate due to fracture. A specialized cutting apparatus, the Soft-Matter, Low-Friction, Y-Shaped Cutting Experimental Rig (SLYCER) is created. With this tool, the fracture behavior of elastomeric binders functionalized with molecular force sensors (mechanophores) is characterized. This includes exploring how stresses are distributed within the material close to the crack tip during cutting to quantify the zone of large deformation. Lastly, Y-shaped cutting is performed to characterize the fracture toughness of additively manufactured propellants. Printed roads, interroad welds, and interlayer welds are targeted to provide a complete description of fracture behavior. Future continuation of this work will focus on varying 3D printing parameters to optimize the fracture toughness of additively manufactured parts. This will lead to improvement of the additive manufacturing process, ensuring mechanical robustness of propellant materials produced in that fashion and moving closer towards commercialization.

Recent advances in additive manufacturing (AM) have established these technologies as effective for producing polymer end-use parts with complex, customized structures and functionalities, expanding their applications across various sectors. Compared to traditional manufacturing methods that rely on machining, molds, and tooling, AM techniques are more cost-effective and offer greater design flexibility.
The final properties of printed parts depend on numerous parameters, including polymer temperatures before extrusion, bed and chamber temperatures, printing speed, and nozzle diameter, among others. This vast array of variables influences the morphology of the printed pieces, affecting characteristics such as porosity, polymer orientation, and crystallinity, which ultimately determine their final properties. Therefore, understanding the structure–processing–property relationship requires careful analysis using a combination of experimental techniques, including X-ray computed tomography, X-ray diffraction (WAXS and/or SAXS), thermal analysis, and mechanical testing.
Our presentation will showcase studies conducted on various polymer materials and AM techniques. Through extensive morphological characterization, we establish connections between processing parameters and final properties, highlighting the impact of factors such as porosity, printing direction, and polymer orientation.[1],[2]

Selective Laser Sintering (SLS) is an advanced additive manufacturing technique that enables the production of intricate polymer components without support structures. While promising, it faces challenges such as defects caused by insufficient particle fusion, material shrinkage, and overheating issues that require deeper understanding [1,2]. This study introduces a combined numerical-experimental approach to investigate the interplay of printing paths and process parameters in sintering dynamics, with a particular emphasis on printing paths. This is particularly important for printer-material combinations that resolve parts into confining contour prints with (solid) infill hatching [2].

The framework utilizes a low-fidelity porous continuum model to simulate heat transfer, material flow, and densification during sintering [3, 4], integrating particle shrinkage and a moving-mesh technique to represent powder bed deformation. Experimental validation performed with a Lisa X SLS 3D printer, featuring straight, single-line and different printing paths, provided new insights into how laser energy density, scanning paths, and process parameters influence sintering quality.

Key findings underscore the significance of localized heat buildup in corner geometries, where oversintering may lead to defects. The study also discusses how refined path planning can mitigate these issues, enhancing process reliability. By merging computational efficiency with experimental data, this work lays a strong foundation for advancing SLS process optimization and offers practical benefits for precision-driven polymer additive manufacturing [5].

Developing thermoplastic nanocomposites (NCs) for 3D printing requires optimizing both material compositions and printing parameters, a time-consuming, labor-intensive process [1]. While machine learning (ML) can optimize printing parameters [2-4], it often overlooks material properties, limiting model effectiveness for specific materials. Here, we propose an ML approach incorporating both material properties and printing parameters to predict printability and quality. To generalize the model for various polymer-filler systems, we prepared NCs using two PLAs with different molecular weights, incorporating graphene nanoplatelets and nanoclay. These variations altered rheological and thermal properties, influencing printing behavior and quality. Next, we characterized key thermal and rheological properties of as-extruded NCs and integrated them into a random forest model while evaluating feature importance. These compositions were then extrusion-printed under varying conditions, and printing quality was measured. Our results [5] highlight the effectiveness of predictive models in capturing complex material-printing relationships. The printability classification model achieved 92.8% accuracy, with flow index (n) and complex viscosity (η∗) as key drivers. Extrusion stability (ΔW) models identified loss factor (tan δ) and printing temperature (T) as the most significant features. The diameter stability (ΔDi) model highlighted the flow consistency index (K), while crystallization (ΔHc) played a larger role in ΔDi prediction than in ΔW. Although the roughness model showed lower performance, it provided insights into the impact of crystallinity, viscosity, and loss factor. Our study highlights machine learning's potential to predict material behavior and optimize processes across various systems.

Additive manufacturing (AM) has emerged as a powerful tool for creating multifunctional devices by integrating advanced nanomaterials into polymer matrices. However, achieving uniform dispersion and strong interfacial bonding between inorganic nanostructures and polymers remains challenging. In our work, we explored surface modification of functional inorganic nanomaterials through polymer grafting as an effective strategy to overcome these limitations for AM applications. Our first approach involves the surface modification of tungsten disulfide (WS₂) nanosheets via surface-initiated ring-opening polymerization (ROP) of ε-caprolactone. These modified WS₂ nanosheets were incorporated into PCL matrices and processed by large format fused granular fabrication (LF-FGF) 3D printing. The grafting significantly enhanced the compatibility between the nanosheets and the polymer matrix, enabling the incorporation of a substantially higher loading of WS₂ compared to unmodified nanosheets. In a complementary study, phosphorescent upconversion nanoparticles (UCNPs) are grafted with modified polyvinyl alcohol (PVA) to facilitate their integration into 3D printed PVA hydrogels, coupled to a chromophore which can bind metal ions leading to the quenching of UNCPs’ emission by Förster Resonance Energy Transfer, for the chemical sensing of metal ions in solution. Overall, these surface modification strategies via polymer grafting offer a versatile route to tailor nanomaterial dispersion and functionality, thereby advancing the development of next-generation polymer composites for additive manufacturing. These results demonstrate that polymer grafting significantly improves nanomaterial dispersion and chemical functionalization and allows for higher filler loadings, thereby enhancing composite performance.