Results of an interlaboratory study on the working curve in vat photopolymerization.

The working curve informs resin properties and print parameters for stereolithography, digital light processing, and other photopolymer additive manufacturing (PAM) technologies. First demonstrated in 1992, the working curve measurement of cure depth vs radiant exposure of light is now a foundational measurement in the field of PAM. Despite its widespread use in industry and academia, there is no formal method or procedure for performing the working curve measurement, raising questions about the utility of reported working curve parameters. Here, an interlaboratory study (ILS) is described in which 24 individual laboratories performed a working curve measurement on an aliquot from a single batch of PAM resin. The ILS reveals that there is enormous scatter in the working curve data and the key fit parameters derived from it. The measured depth of light penetration Dp varied by as much as 7x between participants, while the critical radiant exposure for gelation Ec varied by as much as 70x. This significant scatter is attributed to a lack of common procedure, variation in light engines, epistemic uncertainties from the Jacobs equation, and the use of measurement tools with insufficient precision. The ILS findings highlight an urgent need for procedural standardization and better hardware characterization in this rapidly growing field.

A Data-Driven Approach to Complex Voxel Predictions in Grayscale Digital Light Processing Additive Manufacturing Using U-Nets and Generative Adversarial Networks.

Data-driven U-net machine learning (ML) models, including the pix2pix conditional generative adversarial network (cGAN), are shown to predict 3D printed voxel geometry in digital light processing (DLP) additive manufacturing. A confocal microscopy-based workflow allows for the high-throughput acquisition of data on thousands of voxel interactions arising from randomly gray-scaled digital photomasks. Validation between prints and predictions shows accurate predictions with sub-pixel scale resolution. The trained cGAN performs virtual DLP experiments such as feature size-dependent cure depth, anti-aliasing, and sub-pixel geometry control. The pix2pix model is also applicable to larger masks than it is trained on. To this end, the model can qualitatively inform layer-scale and voxel-scale print failures in real 3D-printed parts. Overall, machine learning models and the data-driven methodology, exemplified by U-nets and cGANs, show considerable promise for predicting and correcting photomasks to achieve increased precision in DLP additive manufacturing.

Synergistic Fire Resistance of Nanobrick Wall Coated 3D Printed Photopolymer Lattices.

Photopolymer additive manufacturing has become the subject of widespread interest in recent years due to its capacity to enable fabrication of difficult geometries that are impossible to build with traditional manufacturing methods. The flammability of photopolymer resin materials and the lattice structures enabled by 3D printing is a barrier to widespread adoption that has not yet been adequately addressed. Here, a water-based nanobrick wall coating is deposited on 3D printed parts with simple (i.e., dense solid) or complex (i.e., lattice) geometries. When subject to flammability testing, the printed parts exhibit no melt dripping and a propensity toward failure at the print layer interfaces. Moving from a simple solid geometry to a latticed geometry leads to reduced time to failure during flammability testing. For nonlatticed parts, the coating provides negligible improvement in fire resistance, but coating of the latticed structures significantly increases time to failure by up to ≈340% compared to the uncoated lattice. The synergistic effect of coating and latticing is attributed to the lattice structures' increased surface area to volume ratio, allowing for an increased coating:photopolymer ratio and the ability of the lattice to better accommodate thermal expansion strains. Overall, nanobrick wall coated lattices can serve as metamaterials to increase applications of polymer additive manufacturing in extreme environments.

Characterizing light engine uniformity and its influence on liquid crystal display based vat photopolymerization printing.

Vat photopolymerization (VP) is a rapidly growing category of additive manufacturing. As VP methods mature the expectation is that the quality of printed parts will be highly reproducible. At present, detailed characterization of the light engines used in liquid crystal display (LCD)-based VP systems is lacking and so it is unclear if they are built to sufficiently tight tolerances to meet the current and/or future needs of additive manufacturing. Herein, we map the irradiance, spectral characteristics, and optical divergence of a nominally 405 nm LCD-based VP light engine. We find that there is notable variation in all of these properties as a function of position on the light engine that cause changes in extent of polymerization and surface texture. We further demonstrate through a derived photon absorption figure of merit and through printed test parts that the spatial heterogeneity observed in the light engine is significant enough to affect part fidelity. These findings help to explain several possible causes of variable part quality and also highlight the need for improved optical performance on LCD-based VP printers.

Microscale Photopatterning of Through-thickness Modulus in a Monolithic and Functionally Graded 3D Printed Part.

3D printing is transforming traditional processing methods for applications ranging from tissue engineering to optics. To fulfill its maximum potential, 3D printing requires a robust technique for producing structures with precise three-dimensional (x, y and z) control of mechanical properties. Previous efforts to realize such spatial control of modulus within 3D printed parts have largely focused on low-resolution (mm to cm scale) multi-material processes and grayscale approaches that spatially vary the modulus in the x-y plane and energy dose-based (E = I 0 t exp) models that do not account for the resin's sub-linear response to irradiation intensity. Here, we demonstrate a novel approach for through-thickness (z) voxelated control of mechanical properties within a single-material, monolithic part. Control over the local modulus is enabled by a predictive model that incorporates the observed non-reciprocal dose response of the material. The model is validated by an application of atomic force microscopy to map the through-thickness modulus on multi-layered 3D parts. Overall, both smooth gradations (30 MPa change over ≈75 µm) and sharp step-changes (30 MPa change over ≈5 µm) in modulus are realized in poly(ethylene glycol) diacrylate based 3D constructs, paving the way for advancements in tissue engineering, stimuli-responsive 4D printing and graded metamaterials.

Digital light processing in a hybrid atomic force microscope: In Situ, nanoscale characterization of the printing process.

Stereolithography (SLA) and digital light processing (DLP) are powerful additive manufacturing techniques that address a wide range of applications including regenerative medicine, prototyping, and manufacturing. Unfortunately, these printing processes introduce micrometer-scale anisotropic inhomogeneities due to the resin absorptivity, diffusivity, reaction kinetics, and swelling during the requisite photoexposure. Previously, it has not been possible to characterize high-resolution mechanical heterogeneity as it develops during the printing process. By combining DLP 3D printing with atomic force microscopy in a hybrid instrument, heterogeneity of a single, in situ printed voxel is characterized. Here, we describe the instrument and demonstrate three modalities for characterizing voxels during and after printing. Sensing Modality I maps the mechanical properties of just-printed, resin-immersed voxels, providing the framework to study the relationships between voxel sizes, print exposure parameters, and voxel-voxel interactions. Modality II captures the nanometric, in situ working curve and is the first demonstration of in situ cure depth measurement. Modality III dynamically senses local rheological changes in the resin by monitoring the viscoelastic damping coefficient of the resin during patterning. Overall, this instrument equips researchers with a tool to develop rich insight into resin development, process optimization, and fundamental printing limits.

Photo-tunable hydrogel mechanical heterogeneity informed by predictive transport kinetics model.

Understanding the three-dimensional (3D) mechanical and chemical properties of distinctly different, adjacent biological tissues is crucial to mimicking their complex properties with materials. 3D printing is a technique often employed to spatially control the distribution of the biomaterials, such as hydrogels, of interest, but it is difficult to print both mechanically robust (high modulus and toughness) and biocompatible (low modulus) hydrogels in a single structure. Moreover, due to the fast diffusion of mobile species during printing and nonequilibrium swelling conditions of low-solids-content hydrogels, it is challenging to form the high-fidelity structures required to mimic tissues. Here a predictive transport and swelling model is presented to model these effects and then is used to compensate for these effects during printing. This model is validated experimentally by photopatterning spatially distinct hydrogel elastic moduli using a single photo-tunable poly(ethylene glycol) (PEG) pre-polymer solution by sequentially patterning and in-diffusing fresh pre-polymer for further polymerization.

Monitoring Fast, Voxel-Scale Cure Kinetics via Sample-Coupled-Resonance Photorheology.

Photopolymerizable materials are the focus of extensive research across a variety of fields ranging from additive manufacturing to regenerative medicine. However, poorly understood material mechanical and rheological properties during polymerization at the relevant exposure powers and single-voxel length-scales limit advancements in part performance and throughput. Here, a novel atomic force microscopy (AFM) technique, sample-coupled-resonance photorheology (SCRPR), to locally characterize the mechano-rheological properties of photopolymerized materials on the relevant reaction kinetic timescales, is demonstrated. By coupling an AFM tip to a photopolymer and exposing the coupled region to a laser, two fundamental photopolymerization phenomena: (1) timescales of photopolymerization at high laser power and (2) reciprocity between photodose and material properties are studied. The ability to capture rapid kinetic changes occurring during polymerization with SCRPR is demonstrated. It is found that reciprocity is only valid for a finite range of exposure powers in the verification material and polymerization is highly localized in a low-diffusion system. After polymerization, in situ imaging of a single polymerized voxel is performed using material-appropriate topographic and nanomechanical modalities of the AFM while still in the as-printed environment.