Evidence map›Paper›PMID 40649440›Full record

ArticleMaterials (Basel, Switzerland)2025

A Dual-Mean Statistical and Multivariate Framework for Machinability Evaluation in CNC Turning: Gradient and Stiffness Analysis Across Five Materials.

Mohammad S Alsoufi

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2025. 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

1 author.

Mohammad S AlsoufiDepartment of Mechanical Engineering, College of Engineering and Architecture, Umm Al-Qura University, Makkah 21955, Saudi Arabia.ORCID 0000-0003-2576-9249

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study proposes a dual-statistical and gradient-based framework to evaluate the machinability of five engineering alloys under CNC turning. Cutting force and surface deformation were measured across five machining zones. Finite difference-based gradients revealed spatial variations in material response. Stainless Steel 304 showed the highest cutting force (328 N), while Aluminum 6061 had the highest deformation (0.0164 mm). Carbon Steel 1020 exhibited the highest force-to-deformation efficiency (>97,000 N/mm). Arithmetic and harmonic means highlighted statistical sensitivities, while principal component analysis (PCA) identified clustering among materials and reduced dimensionality. A composite machinability score, integrating stiffness variation, efficiency gradients, and multivariate features, ranked Aluminum 6061 highest, followed by Brass C26000 and Bronze C51000. This methodology enables interpretable benchmarking and informed material selection in precision manufacturing.

Indexed as

CNC turningcutting forcedeformationefficiencymachinabilityPCAstiffness

Identifiers

PMID40649440
PMCPMC12250742

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.