ArticleScientific reports2022
The design of an inkjet drive waveform using machine learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Functional Nano-to-Microstructures by Jet Printing and Direct Ink Writing.Chemical reviews · 2026Review
- Building the next frontier: Artificial intelligence in 3D-printed medicines.Biomaterials translational · 2026Review
- Optimizing driving waveforms to enhance inkjet printing speed.Scientific reports · 2025Article
- Nonlinear Waveform Optimization for Enhanced Ink Droplet Formation in Material Jetting.Micromachines · 2025Article
- Attentive ink MLP droplet detection algorithm based on the satellite droplets threshold domain.Scientific reports · 2025Article
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Enhancing convergence speed with feature enforcing physics-informed neural networks using boundary conditions as prior knowledge.Scientific reports · 2024Article
- Efficient Creation of Jettability Diagrams Using Active Machine Learning.3D printing and additive manufacturing · 2024Article
- Transfer of a rational formulation and process development approach for 2D inks for pharmaceutical 2D and 3D printing.International journal of pharmaceutics: X · 2024Article
- Machine learning approach to monitor inkjet jetting status based on the piezo self-sensing.Scientific reports · 2023Article
- Multiplex enzymatic synthesis of DNA with single-base resolution.Science advances · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
A drive waveform, which needs to be optimized with ink's fluid properties, is critical to reliable inkjet printing. A generally adopted rule of thumb for its design is mostly dependent on time-consuming and repetitive manual manipulation of its parameters. This work presents a closed-loop machine learning approach to designing an optimal drive waveform for satellite-free inkjet printing at a target velocity. Each of the representative 11 model inks with different fluid properties was ink-jetted with 1100 distinct waveform designs. The high-speed images of their jetting behaviors were acquired and the big datasets of the resulting drop formation and velocity were extracted from the jetting images. Five machine learning models were examined and compared to predict the characteristics of jetting behavior. Among a variety of machine learning models, Multi-layer Perceptron affords the highest prediction accuracy. A closed-loop prediction algorithm that determined the optimal set of waveform parameters for satellite-free drop formation at a target velocity and employed the most superior learning model was established. The proposed method was confirmed through the printing of an unknown model ink with a recommended waveform.
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.