Deep learning-driven optimization and predictive modeling of LASER beam machining for XG3 steel.

Adithya Hegde, Raviraj Shetty, Gururaj Bolar, V Balaji

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

4 authors.

Adithya HegdeDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.

Raviraj ShettyDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India. rr.shetty@manipal.edu.

Gururaj BolarDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.

V BalajiDepartment of Aerospace Engineering, Indian Institute of Science, Bengaluru, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

LASER Beam Machining (LBM) has emerged as a highly precise and non-contact thermal machining process, widely adopted for cutting complex geometries in advanced engineering materials. Its ability to machine difficult-to-cut alloys with minimal mechanical stress makes it particularly suitable for aerospace and defense components. This paper presents an experimental investigation and multi-objective optimization of LASER Beam Machining (LBM) for XG3 steel, a high-performance alloy used in aerospace and defense applications. The study evaluates the impact of four process parameters i.e. cutting speed (8, 10, 12 m/min), gas pressure (0.5, 0.7, 0.9 Bar), focus point (2, 4, 6 mm), and depth of cut (3, 6, 9 mm) on four output responses: surface roughness, machining time, surface hardness, and burr thickness. Experiments were conducted using a Taguchi L

Indexed as

ANOVABPANNLASEROptimizationRSMSurface roughnessTOPSISXG3 steel

Identifiers

PMID41484160

DOI10.1038/s41598-025-34323-6

PMCPMC12858991

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.