Article3D printing and additive manufacturing2025
An Image-Based Transfer Learning Approach for Using
Article in 3D printing and additive manufacturing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Article
- Machine Learning-Assisted Optimisation of the Laser Beam Powder Bed Fusion (PBF-LB) Process Parameters of H13 Tool Steel Fabricated on a Preheated to 350Materials (Basel, Switzerland) · 2026Article
- A big data approach to artificial intelligence driven predictive modelling for optimizing material properties in additive manufacturing.Scientific reports · 2026Article
- Multitechnological integration advances musculoskeletal regeneration: synergistic progress of organoids, 3D/4D bioprinting, single-cell omics and artificial intelligence.Frontiers in bioengineering and biotechnology · 2026Review
- Monitoring, Modeling, and Statistical Analysis in Metal Additive Manufacturing: 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
The mitigation of material defects from additive manufacturing (AM) processes is critical to reliability in their fabricated parts and is enabled by modeling the complex relations between available build monitoring signals and final mechanical performance. To this end, the present study investigates a machine learning approach for predicting mechanical properties for Ti-6Al-4V fabricated through laser powder bed fusion (PBF-LB) AM using
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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.