Dataset Information

Rapid, continuous additive manufacturing by volumetric polymerization inhibition patterning.

ABSTRACT: Contemporary, layer-wise additive manufacturing approaches afford sluggish object fabrication rates and often yield parts with ridged surfaces; in contrast, continuous stereolithographic printing overcomes the layer-wise operation of conventional devices, greatly increasing achievable print speeds and generating objects with smooth surfaces. We demonstrate a novel method for rapid and continuous stereolithographic additive manufacturing by using two-color irradiation of (meth)acrylate resin formulations containing complementary photoinitiator and photoinhibitor species. In this approach, photopatterned polymerization inhibition volumes generated by irradiation at one wavelength spatially confine the region photopolymerized by a second concurrent irradiation wavelength. Moreover, the inhibition volumes created using this method enable localized control of the polymerized region thickness to effect single-exposure, topographical patterning.

SUBMITTER: de Beer MP

PROVIDER: S-EPMC6357759 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Rapid, continuous additive manufacturing by volumetric polymerization inhibition patterning.

de Beer Martin P MP van der Laan Harry L HL Cole Megan A MA Whelan Riley J RJ Burns Mark A MA Scott Timothy F TF

Science advances 20190111 1

Contemporary, layer-wise additive manufacturing approaches afford sluggish object fabrication rates and often yield parts with ridged surfaces; in contrast, continuous stereolithographic printing overcomes the layer-wise operation of conventional devices, greatly increasing achievable print speeds and generating objects with smooth surfaces. We demonstrate a novel method for rapid and continuous stereolithographic additive manufacturing by using two-color irradiation of (meth)acrylate resin form ...[more]

PMID: 30746465

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Project description:Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps-define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to postbuild inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM.

Dataset Information

Rapid, continuous additive manufacturing by volumetric polymerization inhibition patterning.

Publications

Rapid, continuous additive manufacturing by volumetric polymerization inhibition patterning.

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