Efficient Creation of Jettability Diagrams Using Active Machine Learning.

Maryam Pardakhti, Shing-Yun Chang, Qian Yang, Anson W K Ma

Abstract read

In one paragraph

Article in 3D printing and additive manufacturing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Maryam PardakhtiComputer Science and Engineering Department, University of Connecticut, Storrs, Connecticut, USA.ORCID https://orcid.org/0000-0002-9991-0826

Shing-Yun ChangPolymer Program, Institute of Materials Science, University of Connecticut, Storrs, Connecticut, USA.ORCID https://orcid.org/0000-0003-2669-7574

Qian YangComputer Science and Engineering Department, University of Connecticut, Storrs, Connecticut, USA.ORCID https://orcid.org/0000-0001-5519-1092

Anson W K MaPolymer Program, Institute of Materials Science, University of Connecticut, Storrs, Connecticut, USA.ORCID https://orcid.org/0000-0002-2865-5776

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ability to jet a wide variety of materials consistently from print heads remains a key technical challenge for inkjet-based additive manufacturing processes. Drop watching is the most direct method for testing new inks and print head designs but such experiments are also resource consuming. In this work, a data-efficient machine learning technique called active learning is used to construct detailed jettability diagrams that identify complex regions corresponding to "

Indexed as

active learningautonomous 3D printingmachine learningmaterial jettingpredictive modeling

Identifiers

PMID39360143

DOI10.1089/3dp.2023.0023

PMCPMC11443117

What OpenQuestion holds

Textmetadata

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.