Coupled Dimensional Energy Balance and Machine Learning Validation for Ballistic Response Prediction of Fiber Composites

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dc.contributor.authorBeylergil, Bertan
dc.contributor.authorUlus, Hasan
dc.contributor.authorYildiz, Mehmet
dc.date.accessioned2026-01-24T12:31:01Z
dc.date.available2026-01-24T12:31:01Z
dc.date.issued2025
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractIn this study, we present a coupled, dimensional energy-balance model enhanced with machine-learning validation to predict residual-velocity curves and ballistic limits of fiber-reinforced composites. Projectile deceleration is described as a three-term balance involving strength-like, drag-like, and inertial effects, mapped to the nondimensional groups Pi(0), Pi(1), and Pi(2); closed-form and RK4 solutions yield residual velocity and regime boundaries (Pi(0) = Pi(1), Pi(1) = Pi(2)). Validation against six literature datasets (CFRP and aramid laminates; Vr-V0 curves) shows high accuracy: median R2 = 0.93-0.96 and typical RMSE = 10-30 ms(-)1, with best case R2 = 0.976 and RMSE = 6.99 ms(-)1 for thin CFRP. Ballistic-limit predictions accurately capture the nonlinear increase with thickness, with errors less than 1 ms(-)1 in brittle CFRP and up to 10 ms(-)1 in Kevlar laminates. A global master curve of wr = Vr/V0 versus parallel to Pi parallel to 2 collapses all data and shows a consistent trend. Energy-budget analysis quantifies the contributions of the three terms: the strength term Pi(0) dominates in about 90% of operational points, while drag-like effects are minimal and inertial effects only appear at thick or high-velocity limits; the dominance fractions and combined contributions support these shifts. The (V-0,h) regime map, derived by setting Pi(0) = Pi(1) and Pi(1) = Pi(2), separates design-relevant domains and aligns with observed transitions in Vr-V0 modes and slopes. An independent machine-learning check using Random Forests achieves R2 = 0.992, RMSE = 17.5 ms(-)1, and MAE = 12.4 ms(-)1 (fivefold cross-validation: R2 = 0.835 +/- 0.145), supporting the mechanistic hierarchy through feature importance. The integrated physics-based model and machine-learning analysis provide traceable parameters (alpha, beta, gamma), uncertainty bounds, and practical screening maps for composite and geometric options under high-velocity impact.
dc.identifier.doi10.1007/s12221-025-01273-9
dc.identifier.issn1229-9197
dc.identifier.issn1875-0052
dc.identifier.scopus2-s2.0-105023152167
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s12221-025-01273-9
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5595
dc.identifier.wosWOS:001625277900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherKorean Fiber Soc
dc.relation.ispartofFibers and Polymers
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260121
dc.subjectComposite laminates
dc.subjectBallistic impact
dc.subjectResidual-velocity prediction
dc.subjectMechanistic modeling
dc.subjectEnergy balance model
dc.subjectRandom Forest machine learning
dc.titleCoupled Dimensional Energy Balance and Machine Learning Validation for Ballistic Response Prediction of Fiber Composites
dc.typeArticle

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