Hybrid Multimodal Surrogate Modeling and Uncertainty-Aware Co-Design for L-PBF Ti-6Al-4V with Nanomaterials-Informed Morphology Proxies.

Rifath Bin Hossain, Xuchao Pan, Geng Chang, Xin Su, Yu Tao, Xinyi Han

Abstract read

In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Machine Learning and Artificial Intelligence in Metallic Orthopedic Implant Development: A Narrative Review.Materials (Basel, Switzerland) · 2026
Review

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Rifath Bin HossainSchool of Mechanical Engineering, Nanjing University of Science and Technology, No. 200 Xiaolingwei Street, Nanjing 210094, China.ORCID 0009-0004-3185-4014

Xuchao PanSchool of Mechanical Engineering, Nanjing University of Science and Technology, No. 200 Xiaolingwei Street, Nanjing 210094, China.

Geng ChangSchool of Mechanical Engineering, Nanjing University of Science and Technology, No. 200 Xiaolingwei Street, Nanjing 210094, China.ORCID 0009-0005-3529-8112

Xin SuSchool of Mechanical Engineering, Nanjing University of Science and Technology, No. 200 Xiaolingwei Street, Nanjing 210094, China.

Yu TaoSchool of Mechanical Engineering, Nanjing University of Science and Technology, No. 200 Xiaolingwei Street, Nanjing 210094, China.

Xinyi HanSchool of Mechanical Engineering, Nanjing University of Science and Technology, No. 200 Xiaolingwei Street, Nanjing 210094, China.

Funding

Xinjiang Southern Xinjiang Key Industry Innovation 883 and Development 2019DB011

6 · The paper itself

Abstract

Reliable property prediction and process selection in laser powder bed fusion are hindered by small, set-level datasets in which key morphology descriptors are intermittently missing, limiting both generalization and actionable co-design. A hybrid multimodal surrogate strategy is introduced that couples engineered process physics features with morphology proxies through a deployable two-stage embedding module and gradient-boosted tree regressors. Set-resolved inputs are assembled from L-PBF parameters, linear energy density and related energy-density variants, pore and prior-β grain summary statistics, and stress-strain-derived descriptors, followed by missingness-aware feature filtering, median imputation, and 5-fold GroupKFold evaluation grouped by set_id, with morphology embeddings learned on training folds and predicted when absent. Across six targets, the final deployable models achieve an RMSE/R

Indexed as

constrained co-designlaser powder bed fusion (L-PBF)morphology descriptorsmultimodal learningsurrogate modelingTi-6Al-4Vuncertainty quantification

Identifiers

PMID42042699

DOI10.3390/nano16080447

PMCPMC13119149

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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.