ArticleCommunications engineering2024
Towards industry-ready additive manufacturing: AI-enabled closed-loop control for 3D melt electrowriting.
Article in Communications engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Hybrid SES-MEW Scaffold Strategies: A Narrative Review of Multi-Scale Fiber Architectures for Soft and Hard Tissue Engineering.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Green and Scalable Manufacturing of Biodegradable Polymer Scaffolds: Solvent-Free Processing, Supercritical COPolymers · 2026Review
- Patent Landscape of Fiber-Based Fabrication Technologies for Functional Biomaterials: Electrospinning, ForcespinningJournal of functional biomaterials · 2025Review
- Four-Dimensional Printing of Shape Memory Polymers for Biomedical Applications: Advances in DLP and SLA Manufacturing.Polymers · 2025Review
- Engineering Extracellular Microenvironments: The Impact of Fibrous Materials on Cell Behavior.Advanced healthcare materials · 2025Review
- 3D Bioprinted Neural Tissues: Emerging Strategies for Regeneration and Disease Modeling.Pharmaceutics · 2025Review
- Microstructural Effects of Melt Electrowritten-Reinforced Hydrogel Scaffolds for Engineering Thick Skin Substitutes.ACS applied bio materials · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
Melt electrowriting (MEW) is an emerging high-resolution 3D printing technology used in biomedical engineering, regenerative medicine, and soft robotics. Its transition from academia to industry faces challenges such as slow experimentation, low printing throughput, poor reproducibility, and user-dependent operation, largely due to the nonlinear and multiparametric nature of the MEW process. To address these challenges, we applied computer vision and machine learning to monitor and analyze the process in real-time through imaging of the MEW jet between the nozzle-collector gap. To collect data for training we developed an automated data collection methodology that eases the experimental time from days to hours. A feedforward neural network, working in concert with optimization methods and a feedback loop, is used to develop closed-loop control ensuring reproducibility of the printed parts. We demonstrate that machine learning allows streamlining the MEW operation via closed-loop control of the highly nonlinear 3D printing technology.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.