Evidence map›Paper›PMID 38521824›Full record

ArticleScientific reports2024

Optimising the manufacturing of a β-Ti alloy produced via direct energy deposition using small dataset machine learning.

Ryan Brooke, Dong Qiu, Tu Le, Mark A Gibson, Duyao Zhang, Mark Easton

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. 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

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

1 citing paper in PubMed.

  1. 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

6 authors.

Ryan BrookeCentre for Additive Manufacturing, School of Engineering, RMIT University, Melbourne, VIC, 3000, Australia.
Dong QiuCentre for Additive Manufacturing, School of Engineering, RMIT University, Melbourne, VIC, 3000, Australia.
Tu LeCentre for Additive Manufacturing, School of Engineering, RMIT University, Melbourne, VIC, 3000, Australia.
Mark A GibsonCentre for Additive Manufacturing, School of Engineering, RMIT University, Melbourne, VIC, 3000, Australia.
Duyao ZhangCentre for Additive Manufacturing, School of Engineering, RMIT University, Melbourne, VIC, 3000, Australia. duyao.zhang2@rmit.edu.au.
Mark EastonCentre for Additive Manufacturing, School of Engineering, RMIT University, Melbourne, VIC, 3000, Australia.

Funding

Australian Research Council DE210101503Australian Research Council DP220101501
6 · The paper itself

Abstract

Successful additive manufacturing involves the optimisation of numerous process parameters that significantly influence product quality and manufacturing success. One commonly used criteria based on a collection of parameters is the global energy distribution (GED). This parameter encapsulates the energy input onto the surface of a build, and is a function of the laser power, laser scanning speed and laser spot size. This study uses machine learning to develop a model for predicting manufacturing layer height and grain size based on GED constituent process parameters. For both layer height and grain size, an artificial neural network (ANN) reduced error over the data set compared with multi linear regression. Layer height predictions using ANN achieved an R

Indexed as

Additive manufacturingDirect energy depositionMachine learningModellingTitanium alloys

Identifiers

PMID38521824
PMCPMC10960837

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.