Evidence map›Paper›PMID 42045593›Full record

ArticleScientific reports2026

A data-driven approach for high-accuracy tool wear prediction in machining Hastelloy C276.

Mustafa Abdullah, A C Umamaheshwer Rao, T Ramachandran, Swetarani Biswal, Shivam P Chaudhary, Ripendeep Singh, Akanksha Mishra, Vikasdeep Singh Mann, Abhijit Bhowmik, Lema Abate

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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.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Mustafa AbdullahElectric Vehicles Engineering Department, Faculty of Engineering, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan.
A C Umamaheshwer RaoVardhaman College of Engineering, Hyderabad, Telangana, India.
T RamachandranDepartment of Mechanical Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India.
Swetarani BiswalDepartment of Mechanical Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751030, India.
Shivam P ChaudharyDepartment of Mechanical Engineering, Faculty of Engineering, Gokul Global University, Sidhpur, Gujarat, India.
Ripendeep SinghDepartment of Mechanical Engineering, Chandigarh University, Mohali, Punjab, India.
Akanksha MishraDepartment of Mechanical Engineering, Sharda School of Engineering and Sciences, Sharda University, Greater, Noida, India.
Vikasdeep Singh MannDepartment of Mechanical Engineering, School of Engineering and Technology, CGC University, Mohali, Punjab, 140307, India.
Abhijit BhowmikDepartment of Additive Manufacturing, Mechanical Engineering, SIMATS, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, 602105, India.
Lema AbateDepartment of Statistics, College of Natural and Computational Sciences, Mizan-Tepi University, Mizan Teferi, Ethiopia. lema@mtu.edu.et.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hastelloy C276 is extensively used in aerospace, chemical, and high-temperature engineering systems, yet its poor machinability leads to rapid tool degradation and reduced productivity. This study develops a comprehensive machine-learning (ML) framework to model and predict flank-wear progression during the turning of Hastelloy C276 under Dry, Minimum Quantity Lubrication (MQL), and nanoparticle-assisted MQL environments. A series of controlled machining experiments were performed by varying cutting speed, feed, depth of cut, and machining length, generating more than 700 labeled wear samples measured using optical microscopy. Four ML models-Ridge Regression, Decision Tree, Random Forest, and Support Vector Regression- were trained using five-fold cross-validation for hyperparameter optimization, and their final performance was evaluated on an independent test dataset. Among them, Random Forest exhibited the highest predictive accuracy (R

Indexed as

Hastelloy C276Machine LearningRandom ForestSupport Vector RegressionSustainable MachiningTool Wear Prediction

Identifiers

PMID42045593
PMCPMC13287728

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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.