ItemArticle

ANN and machine learning based predictions of MRR in AWSJ machining of CFRP composites

Title
ANN and machine learning based predictions of MRR in AWSJ machining of CFRP composites
Creator
Ramesha, K.; Santhosh, N.; Praveena, B.A.; Nagaraj, Banakara; Naik, N. Channa Keshava; Naveed, Quadri Noorulhasan; Lasisi, Ayodele; Wodajo, Anteneh Wogasso
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.
Source
Scientific Reports;Volume;15;Issue;1;Article No.;14218;
Date
01-01-2025
Publisher
Nature Research
Subject
ANN; AWSJ; CFRP composites; ML models; RSM; XGBoost
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
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 20452322;
Format
online
Language
English
Type
Article

Citation

Ramesha, K.; Santhosh, N.; Praveena, B.A.; Nagaraj, Banakara; Naik, N. Channa Keshava; Naveed, Quadri Noorulhasan; Lasisi, Ayodele; Wodajo, Anteneh Wogasso, “ANN and machine learning based predictions of MRR in AWSJ machining of CFRP composites,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22536.

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