ReviewMaterials (Basel, Switzerland)2020
Artificial Neural Network Algorithms for 3D Printing.
Review in Materials (Basel, Switzerland), 2020. 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.
- Comparative Evaluation of Machine Learning-Assisted Statistical Modeling of Biopolymer Hydrogel Swelling for Material Optimization.ACS omega · 2026Article
- Predictive analysis of tensile strength in FDM Fabricated PLA wood composite materials.Scientific reports · 2026Article
- Evaluating the Performance of 3D-Printed Stab-Resistant Body Armor Using the Taguchi Method and Artificial Neural Networks.Polymers · 2025Article
- Machine learning based approach for surface roughness prediction in precision dental prototyping.Scientific reports · 2025Article
- Use of Computational Intelligence in Customizing Drug Release from 3D-Printed Products: A Comprehensive Review.Pharmaceutics · 2025Review
- Artificial Neural Network Modeling of Mechanical Properties of 3D-Printed Polyamide 12 and Its Fiber-Reinforced Composites.Polymers · 2025Article
- Innovative 3D printing technologies and advanced materials revolutionizing orthopedic surgery: current applications and future directions.Frontiers in bioengineering and biotechnology · 2025Review
- Optimization of Fast Non-Local Means Noise Reduction Algorithm Parameter in Computed Tomographic Phantom Images Using 3D Printing Technology.Diagnostics (Basel, Switzerland) · 2024Article
- Decision Support Tool in the Selection of Powder for 3D Printing.Materials (Basel, Switzerland) · 2024Article
- Emerging Additive Manufacturing Methods for Wearable Sensors: Opportunities to Expand Access to Personalized Health Monitoring.Advanced sensor research · 2024Article
- A Survey of Image-Based Fault Monitoring in Additive Manufacturing: Recent Developments and Future Directions.Sensors (Basel, Switzerland) · 2023Review
- An overview of 3D printed metal implants in orthopedic applications: Present and future perspectives.Heliyon · 2023Review
- Estimation of Head Accelerations in Crashes Using Neural Networks and Sensors Embedded in the Protective Helmet.Sensors (Basel, Switzerland) · 2022Article
- Vibration Fatigue of FDM 3D Printed Structures: The Use of Frequency Domain Approach.Materials (Basel, Switzerland) · 2022Article
- Quality Prediction and Control in Wire Arc Additive Manufacturing via Novel Machine Learning Framework.Micromachines · 2022Article
- A State-of-the-Art Review on Integral Transform Technique in Laser-Material Interaction: Fourier and Non-Fourier Heat Equations.Materials (Basel, Switzerland) · 2021Review
- Advances in 3D Printing for Tissue Engineering.Materials (Basel, Switzerland) · 2021Review
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
Abstract
Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts' properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.
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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.