skip to main content

Title: Design for metrology for freeform optics manufacturing
Freeform optical surfaces offer significant design opportunities but pose new challenges in metrology and manufacturing. Evolution in optics manufacturing processes have changed the surface spatial frequencies that must be measured. Optical surface definition is expected to be with respect to fiducials and datums which must be realizable at all stages of manufacture; uncertainty in that realization becomes important in some cases. Concurrent engineering is required, but appropriate data has not been collated for use by optical designers. One approach to providing such data is described.
Authors:
; ; ; ; ; ;
Award ID(s):
1822049 1338877
Publication Date:
NSF-PAR ID:
10161177
Journal Name:
CIRP
Volume:
84
Issue:
2212-8271
Page Range or eLocation-ID:
169-172
ISSN:
0373-7284
Sponsoring Org:
National Science Foundation
More Like this
  1. There has been an increasing need of technologies to manufacturing chemical and biological sensors for various applications ranging from environmental monitoring to human health monitoring. Currently, manufacturing of most chemical and biological sensors relies on a variety of standard microfabrication techniques, such as physical vapor deposition and photolithography, and materials such as metals and semiconductors. Though functional, they are hampered by high cost materials, rigid substrates, and limited surface area. Paper based sensors offer an intriguing alternative that is low cost, mechanically flexible, has the inherent ability to filter and separate analytes, and offers a high surface area, permeable frameworkmore »advantageous to liquid and vapor sensing. However, a major drawback is that standard microfabrication techniques cannot be used in paper sensor fabrication. To fabricate sensors on paper, low temperature additive techniques must be used, which will require new manufacturing processes and advanced functional materials. In this work, we focus on using aerosol jet printing as a highresolution additive process for the deposition of ink materials to be used in paper-based sensors. This technique can use a wide variety of materials with different viscosities, including materials with high porosity and particles inherent to paper. One area of our efforts involves creating interdigitated microelectrodes on paper in a one-step process using commercially available silver nanoparticle and carbon black based conductive inks. Another area involves use of specialized filter papers as substrates, such as multi-layered fibrous membrane paper consisting of a poly(acrylonitrile) nanofibrous layer and a nonwoven poly(ethylene terephthalate) layer. The poly(acrylonitrile) nanofibrous layer are dense and smooth enough to allow for high resolution aerosol jet printing. With additively fabricated electrodes on the paper, molecularly-functionalized metal nanoparticles are deposited by molecularly-mediated assembling, drop casting, and printing (sensing and electrode materials), allowing full functionalization of the paper, and producing sensor devices with high surface area. These sensors, depending on the electrode configuration, are used for detection of chemical and biological species in vapor phase, such as water vapor and volatile organic compounds, making them applicable to human performance monitoring. These paper based sensors are shown to display an enhancement in sensitivity, as compared to control devices fabricated on non-porous polyimide substrates. These results have demonstrated the feasibility of paper-based printed devices towards manufacturing of a fully wearable, highly-sensitive, and wireless human performance monitor coupled to flexible electronics with the capability to communicate wirelessly to a smartphone or other electronics for data logging and analysis.« less
  2. The goal of this work is to quantify the link between the design features (geometry), in-situ process sensor signatures, and build quality of parts made using laser powder bed fusion (LPBF) additive manufacturing (AM) process. This knowledge is critical for establishing design rules for AM parts, and to detecting impending build failures using in-process sensor data. As a step towards this goal, the objectives of this work are two-fold: Quantify the effect of the geometry and orientation on the build quality of thin-wall features. To explain further, the geometry-related factor is the ratio of the length of a thin-wall (l)more »to its thickness (t) defined as the aspect ratio (length-to-thickness ratio, l/t), and the angular orientation (θ) of the part, which is defined as the angle of the part in the X-Y plane relative to the re-coater blade of the LPBF machine. Assess the thin-wall build quality by analyzing images of the part obtained at each layer from an in-situ optical camera using a convolutional neural network. To realize these objectives, we designed a test part with a set of thin-wall features (fins) with varying aspect ratio from Titanium alloy (Ti-6Al-4V) material — the aspect ratio l/t of the thin-walls ranges from 36 to 183 (11 mm long (constant), and 0.06 mm to 0.3 mm in thickness). These thin-wall test parts were built under three angular orientations of 0°, 60°, and 90°. Further, the parts were examined offline using X-ray computed tomography (XCT). Through the offline XCT data, the build quality of the thin-wall features in terms of their geometric integrity is quantified as a function of the aspect ratio and orientation angle, which suggests a set of design guidelines for building thin-wall structures with LPBF. To monitor the quality of the thin-wall, in-process images of the top surface of the powder bed were acquired at each layer during the build process. The optical images are correlated with the post build quantitative measurements of the thin-wall through a deep learning convolutional neural network (CNN). The statistical correlation (Pearson coefficient, ρ) between the offline XCT measured thin-wall quality, and CNN predicted measurement ranges from 80% to 98%. Consequently, the impending poor quality of a thin-wall is captured from in-situ process data.« less
  3. Abstract. The magnitude and controls of particulate carbon exported from surface watersand its remineralization at depth are poorly constrained. The Carbon FluxExplorer (CFE), a Lagrangian float-deployed imaging sediment trap, has beendesigned to optically measure the hourly variations of particle flux tokilometer depths for months to seasons while relaying data in near-real timeto shore via satellite without attending ships. The main optical proxy forparticle load recorded by the CFE, volume attenuance (VA; units ofmATN cm2), while rigorously defined and highly precise, has not beenrobustly calibrated in terms of particulate organic carbon (POC), nitrogen(PN) and phosphorus (PP). In this study, a novelmore »3-D-printed particle samplerusing cutting edge additive manufacturing was developed and integrated withthe CFE. Two such modified floats (CFE-Cals) were deployed a total of15 times for 18–24 h periods to gain calibration imagery and samples atdepths near 150 m in four contrasting productivity environments during theJune 2017 California Current Ecosystem Long-Term Ecological Research (LTER)process study. Regression slopes for VA : POC and VA : PN (unitsmATN cm2: mmol; R2, n, p value in parentheses) were1.01×104 (0.86, 12, < 0.001) and 1.01×105(0.86, 15, < 0.001), respectively, and were not sensitive toparticle size classes or the contrasting environments encountered. PP was notwell correlated with VA, reflecting the high lability of P relative to C andN. The volume attenuance flux (VAF) to POC flux calibration is compared toprevious estimates.

    « less
  4. Matrix-assisted pulsed laser evaporation (MAPLE) has many benefits over conventional methods (e.g., dip-coating, spin coating, and Langmuir–Blodgett dip-coating) for manufacturing coatings containing pharmacologic agents on medical devices. In particular, the thickness of the coating that is applied to the surface of the medical device can be tightly controlled. In this study, MAPLE was used to deposit rapamycin-polyvinylpyrrolidone (rapamycin-PVP) thin films onto silicon and borosilicate optical glass substrates. Alamar Blue and PicoGreen studies were used to measure the metabolic health and DNA content of L929 mouse fibroblasts as measures of viability and proliferation, respectively. The cells on the MAPLE-deposited rapamycin-PVP surfacesmore »exhibited 70.6% viability and 53.7% proliferation compared to a borosilicate glass control. These data indicate that the antiproliferative properties of rapamycin were maintained after MAPLE deposition.« less
  5. Most modern commodity imaging systems we use directly for photography—or indirectly rely on for downstream applications—employ optical systems of multiple lenses that must balance deviations from perfect optics, manufacturing constraints, tolerances, cost, and footprint. Although optical designs often have complex interactions with downstream image processing or analysis tasks, today’s compound optics are designed in isolation from these interactions. Existing optical design tools aim to minimize optical aberrations, such as deviations from Gauss’ linear model of optics, instead of application-specific losses, precluding joint optimization with hardware image signal processing (ISP) and highly parameterized neural network processing. In this article, we proposemore »an optimization method for compound optics that lifts these limitations. We optimize entire lens systems jointly with hardware and software image processing pipelines, downstream neural network processing, and application-specific end-to-end losses. To this end, we propose a learned, differentiable forward model for compound optics and an alternating proximal optimization method that handles function compositions with highly varying parameter dimensions for optics, hardware ISP, and neural nets. Our method integrates seamlessly atop existing optical design tools, such as Zemax . We can thus assess our method across many camera system designs and end-to-end applications. We validate our approach in an automotive camera optics setting—together with hardware ISP post processing and detection—outperforming classical optics designs for automotive object detection and traffic light state detection. For human viewing tasks, we optimize optics and processing pipelines for dynamic outdoor scenarios and dynamic low-light imaging. We outperform existing compartmentalized design or fine-tuning methods qualitatively and quantitatively, across all domain-specific applications tested.« less