Evidence map›Paper›PMID 36677136›Full record

ArticleMicromachines2022

Evaluating the Stress-Strain Relationship of the Additively Manufactured Lattice Structures.

Long Zhang, Farzana Bibi, Imtiyaz Hussain, Muhammad Sultan, Adeel Arshad, Saqib Hasnain, Ibrahim M Alarifi, Mohammed A Alamir, Uzair Sajjad

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Micromachines, 2022. 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
0.9field-weighted citation impact, top 27% of its field
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, 13 citations in OpenAlex.

  1. Article
  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

9 authors at 7 institutions in 6 countries.

Long ZhangSchool of Metallurgy, Lanzhou Resources & Environment Voc-Tech University, Lanzhou 730123, China.
Farzana BibiDepartment of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Imtiyaz HussainDepartment of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Muhammad SultanDepartment of Agriculture Engineering, Faculty of Agriculture Sciences & Technology, Bahauddin Zakariya University, Multan 60800, Pakistan.ORCID 0000-0002-7301-5567
Adeel ArshadDepartment of Mechanical and Construction Engineering, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK.ORCID 0000-0002-2727-2431
Saqib HasnainSchool of Chemical Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
Ibrahim M AlarifiDepartment of Mechanical and Industrial Engineering, College of Engineering, Majmaah University, Al-Majmaah, Riyadh 11952, Saudi Arabia.ORCID 0000-0003-0275-0975
Mohammed A AlamirDepartment of Mechanical Engineering, College of Engineering, Jazan University, Jazan 45142, Saudi Arabia.ORCID 0000-0002-3813-1773
Uzair SajjadDepartment of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
National Taipei University of Technology · TWBahauddin Zakariya University · PKJazan University · SALanzhou University of Technology · CNMajmaah University · SANorthumbria University · GBUNSW Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extensive amount of research on additively manufactured (AM) lattice structures has been made to develop a generalized model that can interpret how strongly operational variables affect mechanical properties. However, the currently used techniques such as physics models and multi-physics simulations provide a specific interpretation of those qualities, and are not general enough to assess the mechanical properties of AM lattice structures of different topologies produced on different materials via several fabrication methods. To tackle this problem, this study develops an optimal deep learning (DL) model based on more than 4000 data points, which has been optimized by analyzing three different hyper-parameters optimization schemes including gradient boost regression trees (GBRT), gaussian process (GP), and random forest (RF) with different data distribution schemes such as normal distribution, nth root transformation, and robust scaler. With the robust scaler and

Indexed as

additive manufacturingBayesian optimizationdeep learningexplainable artificial intelligencelattice structuremechanical propertiesstress-strain relationship

Identifiers

PMID36677136
PMCPMC9861820
OpenAlexW4313362171

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.