Digital light processing (DLP) as a vat photopolymerization technique is one of the most popular three-dimensional (3D) printing methods, where chains are formed between liquid photocurable resin molecules to crosslink them and solidify the liquid resin using ultraviolet light. The DLP technique is inherently complex and the part accuracy depends on the process parameters that have to be chosen based on the fluid (resin) properties. In the present work, computational fluid dynamics (CFD) simulations are presented for top-down DLP as photocuring 3D printing. The effects of fluid viscosity, travelling speed of build part, travelling speed ratio (ratio of the up-to-down traveling speeds of build part), printed layer thickness, and travel distance considering 13 various cases are scrutinized by the developed model to obtain a stability time of fluid interface. The stability time describes the time it takes for the fluid interface to show minimum fluctuations. According to the simulations, a higher viscosity leads to prints with higher stability time. However, lower stability times in the printed layers are caused by a higher traveling speed ratio (TSR). The variation in settling times with TSR is extremely small in comparison to that of viscosity and travelling speed variations. As a result, a declining trend can be detected for the stability time by increasing the printed layer thickness, while by enhancing the travel distance values, the stability time demonstrated a descending pattern. In total, it was revealed that it is essential to choose optimal process parameters for achieving practical results. Moreover, the numerical model can assist in the optimizing the process parameters.
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http://dx.doi.org/10.3390/polym15112459 | DOI Listing |
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Botany Department, Faculty of Science, Mansoura University, Mansoura, Egypt.
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Sci Rep
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Department of Environmental Engineering, Kwangwoon University, Seoul, 01897, Republic of Korea.
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View Article and Find Full Text PDFNat Med
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Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Predicting whether a patient with cancer will benefit from immune checkpoint inhibitors (ICIs) without resorting to advanced genomic or immunologic assays is an important clinical need. To address this, we developed and evaluated SCORPIO, a machine learning system that utilizes routine blood tests (complete blood count and comprehensive metabolic profile) alongside clinical characteristics from 9,745 ICI-treated patients across 21 cancer types. SCORPIO was trained on data from 1,628 patients across 17 cancer types from Memorial Sloan Kettering Cancer Center.
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