Evidence map›Paper›PMID 40084012›Full record

ArticleHeliyon2025

Performance analysis of steel W18CR4V grinding using RSM, DNN-GA, KNN, LM, DT, SVM models, and optimization via desirability function and MOGWO.

Sofiane Touati, Haithem Boumediri, Yacine Karmi, Mourad Chitour, Khaled Boumediri, Amina Zemmouri, Athmani Moussa, Filipe Fernandes

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2025. 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

8 authors.

Sofiane TouatiMechanical Engineering Department, University Abbes Laghrour, BP. 1252, 40004 Khenchela, Algeria.
Haithem BoumediriMechanical Department, Institute of Applied Science and Technology, University of Constantine 1, Constantine, Algeria.
Yacine KarmiElectromechanical Department, Institute of Applied Science and Technology, University of Constantine 1, Constantine, Algeria.
Mourad ChitourMechanical Engineering Department, University Abbes Laghrour, BP. 1252, 40004 Khenchela, Algeria.
Khaled BoumediriLaboratoire de Recherche en Électromécanique et Sûreté de Fonctionnement, LRESF Laboratory, University of Souk Ahras, P.O. Box 1553, Souk Ahras, 41000, Algeria.
Amina ZemmouriMechanical Engineering Department Badji Mokhtar Annaba University, Mechanics of Materials and Plant Maintenance Research Laboratory (LR3MI), Algeria.
Athmani MoussaMechanical Engineering Department, University Abbes Laghrour, BP. 1252, 40004 Khenchela, Algeria.
Filipe FernandesCIDEM, ISEP - Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015, Porto, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents an innovative approach to optimizing the grinding process of W18CR4V steel, a high-performance material used in reamer manufacturing, using advanced machine learning models and multi-objective optimization techniques. The novel combination of Deep Neural Networks with Genetic Algorithm (DNN-GA), K-Nearest Neighbors (KNN), Levenberg-Marquardt (LM), Decision Trees (DT), and Support Vector Machines (SVM) was employed to predict key process outcomes, such as surface roughness (Ra), maximum roughness height (Rz), and production time. The results reveal significant improvements, with Ra values ranging from 0.231 μm to 1.250 μm (up to 81.5 % reduction) and Rz from 1.519 μm to 6.833 μm (up to 77.7 % reduction). The hybrid DNN-GA model achieved R

Indexed as

Desirability functionGrinding processMachine learning modelsMulti-objective grey wolf optimization (MOGWO)Surface roughnessW18CR4V steel

Identifiers

PMID40084012
PMCPMC11904558

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.