Evidence map›Paper›PMID 40129694›Full record

ArticleDiscover mechanical engineering2025

Data-driven approaches for predicting mechanical properties and determining processing parameters of selective laser sintered nylon-12 components.

Ruixuan Tu, Candice Majewski, Inna Gitman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Discover mechanical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ruixuan TuDivision of Surgery and Interventional Science, University College London, London, UK.ORCID 0000-0001-7610-4138
Candice MajewskiDepartment of Mechanical Engineering, University of Sheffield, Sheffield, UK.ORCID 0000-0003-3324-3511
Inna GitmanDepartment of Mechanics of Solids, Surfaces & Systems, University of Twente, Enschede, The Netherlands.ORCID 0000-0002-7369-6905

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to allow engineers to make decisions regarding laser settings in selective laser sintering and predict the mechanical properties of materials, conventional material models could provide accurate solutions and recommendations, however, they are potentially expensive and time-consuming. Thus, a number of computational data-driven methodologies have been introduced in this article, as alternatives, to formulate cross-correlations between the processing parameters and mechanical properties of selective laser sintered (SLS) nylon-12 components. Proposed in this article Supplementary Information: The online version contains supplementary material available at 10.1007/s44245-025-00094-7.

Indexed as

Adaptive neural fuzzy inference systemFuzzy inference systemNeural networksPolymer additive manufacturingSelective laser sintering

Identifiers

PMID40129694
PMCPMC11929718

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.