AI-driven models for prediction of ceramic tiles' properties and detection of the influences: Behavior during shaping and drying.

Milica V Vasić, Paul O Awoyera, Keneddy C Onyelowe, Md Azree Othuman Mydin, Ivana Barišić, Ivanka Netinger Grubeša

Abstract read

In one paragraph

Article in Science progress. 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

6 authors.

Milica V VasićCentre for Materials, Institute for Testing of Materials, Beograd, Serbia.ORCID 0000-0002-9130-6720

Paul O AwoyeraDepartment of Civil Engineering, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia.ORCID 0000-0002-6212-5090

Keneddy C OnyeloweDepartment of Civil Engineering, College of Engineering & Engineering Technology, Michael Okpara University of Agriculture, Umudike, Nigeria.

Md Azree Othuman MydinSchool of Housing, Building and Planning, Universiti Sains Malaysia, Penang, Malaysia.

Ivana BarišićFaculty of Civil Engineering and Architecture Osijek, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia.

Ivanka Netinger GrubešaDepartment for Civil Engineering, University North, Varaždin, Croatia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The shaping and drying of ceramics are a critical yet complex process that directly influences ceramic materials' final properties and performance. Predicting key parameters such as the coefficient of plasticity, mass loss during drying in the air at the critical point, and shaping moisture is essential for optimizing these processes. This study analyzes the dataset of the clays of various chemical compositions to predict and reveal the most important influences on the shaping and drying parameters in producing ceramic tiles. The data are then employed to develop and compare four advanced machine learning models. The models were evaluated using the most important performance metrics such as the coefficient of determination (

Indexed as

Ceramic tileschemical compositionmachine learningpredictive modelssensitivity analysissustainability

Identifiers

PMID40448329

DOI10.1177/00368504251348050

PMCPMC12126631

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC

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.