ArticleNature communications2022
Generalisable 3D printing error detection and correction via multi-head neural networks.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Residual Stresses and Distortion in Material Extrusion Additive Manufacturing of Reinforced Thermoplastic Composites: A Review.Polymers · 2026Review
- Fast Image Segmentation Toward Automation of 3D Ice Printing.Chemical & biomedical imaging · 2026Article
- Hybrid reasoning for perception, explanation, and autonomous action in manufacturing.Nature communications · 2026Article
- Review
- Universal Solvent Escape Strategies for Efficient Curing of Hydrogen-Bond-Rich 3D Printing Inks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Review of Material Processing Technology for 3D Concrete Printing.Materials (Basel, Switzerland) · 2026Review
- Deep Learning-Based Super Resolution Applied to Finite Element Analysis of Fused Deposition Modeling 3D Printing.3D printing and additive manufacturing · 2025Article
- Bridging Additive Manufacturing and Electronics Printing in the Age of AI.Nanomaterials (Basel, Switzerland) · 2025Review
- Article
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Bayesian optimization with Gaussian-process-based active machine learning for improvement of geometric accuracy in projection multi-photon 3D printing.Light, science & applications · 2025Article
- Continuous Multi-Material Additive Manufacturing.Research (Washington, D.C.) · 2025Article
- Insights into geometric deviations of medical 3d-printing: a phantom study utilizing error propagation analysis.3D printing in medicine · 2024Article
- Neural network-enabled, all-electronic control of non-Newtonian fluid flow.Applied physics letters · 2024Article
- On-Machine LTS Integration for Layer-Wise Surface Quality Characterization in MEX/P.Sensors (Basel, Switzerland) · 2024Article
- FFF print defect characterization through in-situ electrical resistance monitoring.Scientific reports · 2024Article
- Online Measurement for Parameter Discovery in Fused Filament Fabrication.Integrating materials and manufacturing innovation · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Material extrusion is the most widespread additive manufacturing method but its application in end-use products is limited by vulnerability to errors. Humans can detect errors but cannot provide continuous monitoring or real-time correction. Existing automated approaches are not generalisable across different parts, materials, and printing systems. We train a multi-head neural network using images automatically labelled by deviation from optimal printing parameters. The automation of data acquisition and labelling allows the generation of a large and varied extrusion 3D printing dataset, containing 1.2 million images from 192 different parts labelled with printing parameters. The thus trained neural network, alongside a control loop, enables real-time detection and rapid correction of diverse errors that is effective across many different 2D and 3D geometries, materials, printers, toolpaths, and even extrusion methods. We additionally create visualisations of the network's predictions to shed light on how it makes decisions.
Indexed as
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.