Machine learning-assisted evolutionary optimization of hole quality and surface integrity in abrasive waterjet drilling of polycarbonate.

J Bharani Chandar, C Rathinasuriyan, N Lenin, M Sivakumar, Robert Čep, Sachin Salunkhe

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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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Authors and funding

6 authors.

J Bharani ChandarDepartment of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Morai, Tamil Nadu, 600 062, India.

C RathinasuriyanDepartment of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Morai, Tamil Nadu, 600 062, India. rathinasuriyanphd@gmail.com.

N LeninDepartment of Mechanical Engineering, SRM TRP Engineering College, Mannachanallur, Tamil Nadu, 621 105, India.

M SivakumarDepartment of Mechanical Engineering, SRM TRP Engineering College, Mannachanallur, Tamil Nadu, 621 105, India.

Robert ČepDepartment of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00, Ostrava, Czech Republic.

Sachin SalunkheDepartment of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Abrasive Waterjet Machining (AWJM) is a reliable non-traditional technology for machining polymer-based engineered materials with low thermal degradation, dimensional inaccuracy, and surface damage. This study examines polycarbonate hole drilling performance using 125 full-factorial experimental trials using process parameters water pressure, standoff distance, and traverse rate. With a coordinate measuring machine and stylus-based profilometer, kerf angle, entry and exit circularity, and surface roughness were measured in a detailed metrological research. Four ML models were created to develop accurate predicting capabilities, with the Random Forest (RF) model performing best in all responses. RF predicted processes accurately with high coefficient-of-determination R

Indexed as

Abrasive waterjet drillingMachine learningOptimizationProcess parameter

Identifiers

PMID41807609

DOI10.1038/s41598-026-42482-3

PMCPMC13099977

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LicenceCC BY-NC-ND

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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.