ArticlePolymers2023
Computational Fluid Dynamics Modeling of Top-Down Digital Light Processing Additive Manufacturing.
Article in Polymers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Computational approaches in bioprinting processes.Nature reviews bioengineering · 2026Article
- Personalized Nasal Protective Devices: Importance and Perspectives.Life (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
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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Registered trials
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