Comparative machinability and neural network-driven optimization of AISI D2 steel using chamfered and conventional tooling.

Muhammad Sana, Muhammad Umar Farooq, Saqib Anwar

Abstract read

In one paragraph

Article in Scientific reports, 2026. 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

3 authors.

Muhammad SanaNorm Asbjornson College of Engineering, Department of Mechanical and Industrial Engineering, Montana State University, Bozeman, 59717, MT, USA.ORCID https://orcid.org/0000-0003-1613-4188

Muhammad Umar FarooqDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA. umarmuf0@gmail.com.

Saqib AnwarIndustrial Engineering Department, College of Engineering, King Saud University, 11421, Riyadh, Saudi Arabia. sanwar@ksu.edu.sa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dry turning of AISI D2 steel requires a balance between productivity, surface integrity, thermal loading, and energy demand. This study compares the machinability of a chamfered Xcel insert and a conventional carbide insert during dry turning of AISI D2 steel using a 2

Indexed as

AISI D2Aritificial neural networkMachine learningOptimizationTooling designTurning

Identifiers

PMID42289457

DOI10.1038/s41598-026-52892-y

PMCPMC13526878

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.