journal article·2026-06

Effect of indenter geometry and machine-learning-based prediction on the quasi-static indentation response of stitched carbon-glass quadraxial laminates

Avinash Thirunavukkarasu, Chandrasekar Muthukumar, Sanjay Dakshinamoorthy

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OpenAlex topics: Mechanical Behavior of Composites · Metal and Thin Film Mechanics · Epoxy Resin Curing Processes

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References (14)

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1. Bhudolia SK, Kam KKC, Perrotey P, Joshi SC (2019) Effect of fixation stitches on out-of-plane response of textile non-crimp fabric composites. J Ind Text 48(7):1151–1166. https://doi.org/10.1177/152808371875752510.1177/1528083718757525 ↗
2. García-Rodríguez SM, Costa J, Singery V, Boada I, Mayugo JA (2018) The effect interleaving has on thin-ply non-crimp fabric laminate impact response: X-ray tomography investigation. Compos Part Appl Sci Manuf 107:409–420. https://doi.org/10.1016/j.compositesa.2018.01.02310.1016/j.compositesa.2018.01.023 ↗
3. Korkmaz M (2024) Investigation of the bending and quasi-static indentation properties of three‐dimensional (3D) textile‐reinforced composites. Polym Compos 45(2):1703–1714. https://doi.org/10.1002/pc.2788310.1002/pc.27883 ↗
4. Osa-Uwagboe N, Udu AG, Silberschmidt VV, Baxevanakis KP, Demirci E (2024) Effects of seawater on mechanical performance of composite sandwich structures: a machine learning framework. Mater (Basel) 17(11):2549. https://doi.org/10.3390/ma1711254910.3390/ma17112549 ↗
5. Kumar SS et al (2025) Investigation and machine learning-based prediction of mechanical properties in hybrid natural fiber composites. Sci Rep 15(1):33700. https://doi.org/10.1038/s41598-025-18944-510.1038/s41598-025-18944-5 ↗
6. Oh HT, Won JI, Woo SC, Kim TW (2020) Determination of impact damage in CFRP via PVDF signal analysis with support vector machine. Mater (Basel) 13(22):5207. https://doi.org/10.3390/ma1322520710.3390/ma13225207 ↗
7. Guo F, Li W, Jiang P, Chen F, Yang C (2023) Deep learning for time series-based acoustic emission damage classification in composite materials. Russ J Nondestruct Test 59(6):665–676. https://doi.org/10.1134/S106183092360031410.1134/S1061830923600314 ↗
8. Bischl B et al (2023) Hyperparameter optimization: foundations, algorithms, best practices, and open challenges. Wiley Interdiscip Rev Data Min Knowl Discov 13(2):1484. https://doi.org/10.1002/widm.148410.1002/widm.1484 ↗
9. Pedregosa F et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830. https://doi.org/10.1007/978-3-319-65340-2_2810.1007/978-3-319-65340-2_28 ↗
10. Sheini Dashtgoli D, Taghizadeh S, Macconi L, Concli F (2024) Comparative analysis of machine learning models for predicting the mechanical behavior of bio-based cellular composite sandwich structures. Mater (Basel) 17(14):3493. https://doi.org/10.3390/ma1714349310.3390/ma17143493 ↗
11. Yoganandam K, Premkumar K, Alagumalai V, Lin J, Shanmugam V, Das O (2023) Quasi-static puncture shear loading behaviour MWCNT/glass fibre epoxy laminates with various indenters. Polym Test 127:108191. https://doi.org/10.1016/j.polymertesting.2023.10819110.1016/j.polymertesting.2023.108191 ↗
12. Bulut M, Erkliğ A (2018) The investigation of quasi-static indentation effect on laminated hybrid composite plates. Mech Mater 117:225–234. https://doi.org/10.1016/j.mechmat.2017.11.00510.1016/j.mechmat.2017.11.005 ↗
13. Osa-Uwagboe N, Udu AG, Silberschmidt VV, Baxevanakis KP, Demirci E (2019) Damage assessment of glass-fibre-reinforced plastic structures under quasi-static indentation with acoustic emission. Mater (Basel) 16(14):5036. https://doi.org/10.3390/ma1614503610.3390/ma16145036 ↗
14. Khan A, Kim N, Shin JK, Kim HS, Youn BD (2019) Damage assessment of smart composite structures via machine learning: a review. JMST Adv 1(1):107–124. https://doi.org/10.1007/s42791-019-0012-210.1007/s42791-019-0012-2 ↗