ArticleMicromachines2024
Deep Learning-Driven Prediction of Mechanical Properties of 316L Stainless Steel Metallographic by Laser Powder Bed Fusion.
Article in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Data-driven prediction of hardness and layer behavior in YbSi-mullite-Si environmental barriers.Journal of computer-aided molecular design · 2026Article
- Machine Learning and Artificial Intelligence in Metallic Orthopedic Implant Development: A Narrative Review.Materials (Basel, Switzerland) · 2026Review
- Machine Learning-Enabled Layer-Wise Melting Quality Recognition for Laser Powder Bed Fusion Process via In Situ Monitoring.Materials (Basel, Switzerland) · 2026Article
- Machine-Learning-Enabled Hydrogel Biosensors for Wearable Health Monitoring.Gels (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Metal Additive Manufacturing: Applications in Design, Process Modeling, Monitoring, and Quality Optimization.Materials (Basel, Switzerland) · 2026Review
- Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.Gels (Basel, Switzerland) · 2025Review
- Microstructural and Mechanical Property Variations in 316L Stainless Steel Fabricated by Laser Powder Bed Fusion Under High-Density Processing Conditions.Materials (Basel, Switzerland) · 2025Article
- Manufacturing, Microstructure, and Mechanics of 316L SS Biomaterials by Laser Powder Bed Fusion.Journal of functional biomaterials · 2025Article
- Machine Learning in Gel-Based Additive Manufacturing: From Material Design to Process Optimization.Gels (Basel, Switzerland) · 2025Review
- Iron-Based High-Temperature Alloys: Alloying Strategies and New Opportunities.Materials (Basel, Switzerland) · 2025Review
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
This study developed a new metallography-property relationship neural network (MPR-Net) to predict the relationship between the microstructure and mechanical properties of 316L stainless steel built by laser powder bed fusion (LPBF). The accuracy R
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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.