ANN and machine learning based predictions of MRR in AWSJ machining of CFRP composites
subject: ANN; AWSJ; CFRP composites; ML models; RSM; XGBoost
description: 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.
creator: Ramesha, K.; Santhosh, N.; Praveena, B.A.; Nagaraj, Banakara; Naik, N. Channa Keshava; Naveed, Quadri Noorulhasan; Lasisi, Ayodele; Wodajo, Anteneh Wogasso
source: Scientific Reports;Volume;15;Issue;1;Article No.;14218;
publisher: Nature Research
date: 01-01-2025
rights: All Open Access; Gold Open Access; Green Open Access
relation: ISSN: 20452322;
format: online
language: English
type: Article
coverage: 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