ArticleMaterials (Basel, Switzerland)2024
Enhancing Fused Deposition Modeling Precision with Serial Communication-Driven Closed-Loop Control and Image Analysis for Fault Diagnosis-Correction.
Article in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Optimization of print parameters for batch and continuous manufacturing of three-dimensional (3D) printed dosage forms using artificial intelligence and machine learning.Drug delivery and translational research · 2026Article
- TranslatingFrontiers in bioengineering and biotechnology · 2026Article
- Application of Numerical Modeling and Finite Element Analysis in Fused Filament Fabrication: A Review.Materials (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Additive manufacturing (AM) also commonly known as 3D printing is an advanced technique for manufacturing complex three-dimensional (3D) parts by depositing raw material layer by layer. Various sub-categories of additive manufacturing exist including directed energy deposition (DED), powder bed fusion (PBF), and fused deposition modeling (FDM). FDM has gained widespread adoption as a popular method for manufacturing 3D parts, even for heavy-duty industrial applications. However, challenges remain, particularly regarding part quality. Print parameters such as print speed, nozzle temperature, and flow rate can significantly impact the final product's quality. To address this, implementing a closed-loop quality control system is essential. This system consistently monitors part surface quality during printing and adjusts print parameters upon defect detection. In this study, we propose a simple yet effective image analysis-based closed-loop control system, utilizing serial communication and Python v3.12, a widely accessible software platform. The system's accuracy and robustness are evaluated, demonstrating its effectiveness in ensuring FDM-printed part quality. Notably, this control system offers superior speed in restoring part quality to normal upon defect detection and is easily implementable on commercially available FDM 3D printers, fostering decentralized quality manufacturing.
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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.