Data description This dataset presents the raw and augmented data that were used to train the machine learning (ML) models for classification of printing outcome in projection two-photon lithography (P-TPL). P-TPL is an additive manufacturing technique for the fabrication of cm-scale complex 3D structures with features smaller than 200 nm. The P-TPL process is further described in this article: “Saha, S. K., Wang, D., Nguyen, V. H., Chang, Y., Oakdale, J. S., and Chen, S.-C., 2019, "Scalable submicrometer additive manufacturing," Science, 366(6461), pp. 105-109.” This specific dataset refers to the case wherein a set of five line features were projected and the printing outcome was classified into three classes: ‘no printing’, ‘printing’, ‘overprinting’. Each datapoint comprises a set of ten inputs (i.e., attributes) and one output (i.e., target) corresponding to these inputs. The inputs are: optical power (P), polymerization rate constant at the beginning of polymer conversion (kp-0), radical quenching rate constant (kq), termination rate constant at the beginning of polymer conversion (kt-0), number of optical pulses, (N), kp exponential function shape parameter (A), kt exponential function shape parameter (B), quantum yield of photoinitiator (QY), initial photoinitiator concentration (PIo), and the threshold degree of conversion (DOCth). The output variable is ‘Class’ which can take these three values: -1 for the class ‘no printing’, 0 for the class ‘printing’, and 1 for the class ‘overprinting’. The raw data (i.e., the non-augmented data) refers to the data generated from finite element simulations of P-TPL. The augmented data was obtained from the raw data by (1) changing the DOCth and re-processing a solved finite element model or (2) by applying physics-based prior process knowledge. For example, it is known that if a given set of parameters failed to print, then decreasing the parameters that are positively correlated with printing (e.g. kp-0, power), while keeping the other parameters constant would also lead to no printing. Here, positive correlation means that individually increasing the input parameter will lead to an increase in the amount of printing. Similarly, increasing the parameters that are negatively correlated with printing (e.g. kq, kt-0), while keeping the other parameters constant would also lead to no printing. The converse is true for those datapoints that resulted in overprinting. The 'Raw.csv' file contains the datapoints generated from finite element simulations, the 'Augmented.csv' file contains the datapoints generated via augmentation, and the 'Combined.csv' file contains the datapoints from both files. The ML models were trained on the combined dataset that included both raw and augmented data.
more »
« less
Minimizing Shrinkage in Microstructures Printed With Projection Two-Photon Lithography
Two-photon lithography (TPL) is a photopolymerization-based additive manufacturing technique capable of fabricating complex 3D structures with submicron features. Projection TPL (P-TPL) is a specific implementation that leverages projection-based parallelization to increase the rate of printing by three orders of magnitude. However, a practical limitation of P-TPL is the high shrinkage of the printed microstructures that is caused by the relatively low degree of polymerization in the as-printed parts. Unlike traditional stereolithography (SLA) methods and conventional TPL, most of the polymerization in P-TPL occurs through dark reactions while the light source is off, thereby resulting in a lower degree of polymerization. In this study, we empirically investigated the parameters of the P-TPL process that affect shrinkage. We observed that the shrinkage reduces with an increase in the duration of laser exposure and with a reduction of layer spacing. To broaden the design space, we explored a photochemical post-processing technique that involves further curing the printed structures using UV light while submerging them in a solution of a photoinitiator. With this post-processing, we were able to reduce the areal shrinkage from more than 45% to 1% without limiting the geometric design space. This shows that P-TPL can achieve high dimensional accuracy while taking advantage of the high throughput when compared to conventional serial TPL. Furthermore, P-TPL has a higher resolution when compared to the conventional SLA prints at a similar shrinkage rate.
more » « less- Award ID(s):
- 2045147
- NSF-PAR ID:
- 10391835
- Date Published:
- Journal Name:
- ASME 2022 17th International Manufacturing Science and Engineering Conference
- Page Range / eLocation ID:
- MSEC2022-86076
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Two-photon lithography (TPL) is a direct laser writing process that enables the fabrication of cm-scale complex three-dimensional polymeric structures with submicrometer resolution. In contrast to the slow and serial writing scheme of conventional TPL, projection TPL (P-TPL) enables rapid printing of entire layers at once. However, process prediction remains a significant challenge in P-TPL due to the lack of computationally efficient models. In this work, we present machine learning-based surrogate models to predict the outcomes of P-TPL to >98% of the accuracy of a physics-based reaction-diffusion finite element simulation. A classification neural network was trained using data generated from the physics-based simulations. This enabled us to achieve computationally efficient and accurate prediction of whether a set of printing conditions will result in precise and controllable polymerization and the desired printing versus no printing or runaway polymerization. We interrogate this surrogate model to investigate the parameter regimes that are promising for successful printing. We predict combinations of photoresist reaction rate constants that are necessary to print for a given set of processing conditions, thereby generating a set of printability maps. The surrogate models reduced the computational time that is required to generate these maps from more than 10 months to less than a second. Thus, these models can enable rapid and informed selection of photoresists and printing parameters during process control and optimization.more » « less
-
more » « less
Abstract Optical lenses require feature resolution and surface roughness that are beyond most (3D) printing methods. A new continuous projection‐based vat photopolymerization process is reported that can directly shape polymer materials into optical lenses with microscale dimensional accuracy (< 14.7 µm) and nanoscale surface roughness (< 20 nm) without post‐processing. The main idea is to utilize frustum layer stacking, instead of the conventional 2.5D layer stacking, to eliminate staircase aliasing. A continuous change of mask images is achieved using a zooming‐focused projection system to generate the desired frustum layer stacking with controlled slant angles. The dynamic control of image size, objective and imaging distances, and light intensity involved in the zooming‐focused continuous vat photopolymerization are systematically investigated. The experimental results reveal the effectiveness of the proposed process. The 3D‐printed optical lenses with various designs, including parabolic lenses, fisheye lenses, and a laser beam expander, are fabricated with a surface roughness of 3.4 nm without post‐processing. The dimensional accuracy and optical performance of the 3D‐printed compound parabolic concentrators and fisheye lenses within a few millimeters are investiagted. These results highlight the rapid and precise nature of this novel manufacturing process, demonstrating a promising avenue for future optical component and device fabrication.
-
Desktop 3D printing stereolithography (SLA) is a fabrication technique based on photopolymerization that can be used to efficiently create novel reaction devices for laboratory geochemistry with complex features (e.g. internal channels, small volumes) that are beyond the capabilities of traditional machining methods. However, the stability of 3D printed parts for low-temperature aqueous geochemical conditions has not been carefully evaluated. Furthermore, it is unclear what criteria should be used when attempting to optimize the mechanical and chemical properties during post-processing steps. Addressing these challenges is important for determining the suitability of 3D printed devices for laboratory investigations such as mineral precipitation/dissolution ex- periments. Here, we use thermogravimetric analysis (TGA) profiles, dynamic mechanical analysis (DMA), and chemical extraction of leachables to show how ultraviolet (UV) post-curing can optimize properties of a com- mercial photo-reactive resin (Formlabs Standard Clear). The mechanical and chemical stability of the post-cured material was enhanced and a working temperature of up to 80 °C was determined. We further provide data showing the stability and compatibility of the material in aqueous conditions of pH 0, 5.7 and 12. As SLA 3D printing is still an emerging and rapidly developing technology, the method presented here will provide a fra- mework for assessing how new printer types and materials (i.e. resins) impact the suitability of SLA printed devices for future experimental studies.more » « less
-
more » « less
3D‐printed polymer blends with programmable mechanical and compositional heterogeneity were fabricated using grayscale digital light processing by spatially modulating the intensity of light during printing and swelling the resulting part with a second monomer. A rubbery poly(ethylene glycol) diacrylate functionally graded print is variably swollen with acrylamide monomer as a function of crosslinking density. Following a secondary polymerization, a 3D‐printed functionally graded blend with regions of varying composition and stiffness was formed. A deterministic model for polymer conversion informs printing conditions to correspond with predicted material properties based upon local volume fractions of the two materials. Upon the secondary polymerization, two networks are present within the printed structure including glassy and rubbery regions. The compressive moduli of local regions within prints ranges from 76 to 200 MPa and measured moduli of the structures agree with predicted values acquired using finite element analysis. A lattice structure with prescribed local stiffness printed using grayscale exposures deforms differentially when compressed. Advantageously, local dimensional deformations caused by the removal of the unreacted printing monomer are eliminated due to the introduction of the second polymer. This method provides predictive control over local mechanical properties and high shape precision while maintaining the simplicity of vat photopolymerization.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/MSEC2022-86076
