Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ramesha, K.; Santhosh, N.; Praveena, B.A.; Nagaraj, Banakara; Naik, N. Channa Keshava; Naveed, Quadri Noorulhasan; Lasisi, Ayodele; Wodajo, Anteneh Wogasso Title A name given to the resource ANN and machine learning based predictions of MRR in AWSJ machining of CFRP composites Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Scientific Reports;Volume;15;Issue;1;Article No.;14218; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1038/s41598-025-92602-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1038/s41598-025-92602-8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003423917?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003423917?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ramesha K., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, 560 074, India; Santhosh N., Department of Mechanical Engineering, MVJ College of Engineering, Near ITPB, Whitefield, Bangalore, 560 067, India; Praveena B.A., Department of Mechanical Engineering, Nitte Meenakshi Institute of Technology, Bangalore, India; Nagaraj B., Department of Mechanical Engineering, Ballari Institute of Technology and Management, Ballari, India; Naik N.C.K., Department of Mechanical Engineering, BGS College of Engineering and Technology, Bangalore, India; Naveed Q.N., Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia; Lasisi A., Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia; Wodajo A.W., Department of Automotive Engineering, College of Engineering and Technology, Dilla University, Dilla, Ethiopia Description An account of the resource This study investigates the effectiveness of Abrasive Water Suspension Jet (AWSJ) Machining, a non-conventional erosion-based method, for machining carbon fiber-reinforced polymer (CFRP) composites. The focus was on analyzing key process parametersabrasive size, feed rate, and standoff distance (SOD)under submerged cutting conditions and their impact on material removal rate (MRR), kerf width, and surface roughness. Experimental trials were conducted, and advanced computational techniques, including Response Surface Methodology (RSM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), were used for parameter optimization and predictive analysis. The results showed that submerged cutting significantly improved machining quality by reducing surface roughness and ensuring uniform kerf widths. Increasing the jet diameter in underwater conditions stabilized the nozzle, leading to smoother and more precise cuts. Among the predictive models, XGBoost demonstrated the highest accuracy and efficiency in forecasting MRR, while Random Forest and ANN provided competitive performance. The integration of RSM and machine learning (ML) techniques enabled effective optimization of machining parameters, showcasing the potential for cost-effective and high-precision CFRP machining. These findings are particularly relevant for industries like aerospace and automotive, where machining efficiency and precision are crucial. The Author(s) 2025. Subject The topic of the resource ANN; AWSJ; CFRP composites; ML models; RSM; XGBoost Publisher An entity responsible for making the resource available Nature Research Relation A related resource ISSN: 20452322; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online