Machine learning and shap-based prediction of multifactor thermal-oxidative aging behavior in glass fiber reinforced polyamide6

Abstract

In this study, glass fiber-reinforced polyamide 6 (PA6-GF) was selected as a representative system to establish a predictive framework for thermal-oxidative aging behavior. Through nonlinear fitting of traditional empirical models followed by the evaluation of twelve machine learning algorithms, it was found that the conventional models exhibited limited predictive accuracy and generalization capability, whereas the machine learning approaches were able to more effectively capture the complex nonlinear interactions among temperature, oxygen partial pressure, specimen thickness, and aging time. To further elucidate the underlying mechanisms, SHapley Additive exPlanations (SHAP) analysis was employed, highlighting the distinct roles and relative contributions of aging time, oxygen partial pressure, temperature, and thickness in governing the thermal-oxidative aging process. These findings enhance the understanding of multi-factor aging mechanisms and provide practical guidance for improving the long-term durability and reliability of engineering components operating under complex service conditions.

Keywords

Glass fiber-reinforced polyamide6 / multi-factor coupling / machine learning / thermal-oxidative aging / SHapley Additive exPlanations

References

[1] Celina M. C.. Review of polymer oxidation and its relationship with materials performance and lifetime prediction Polym. Degrad. Stab. 2013 98 2419 29

[2] Li D.,Xu S.,Li J.. Effect of thermal oxygen aging on the crystallization and insulation properties of polyether ether ketone J. Appl. Polym. Sci. 2023 140 e53745

[3] Ghaderi A.,Ayoub G.,Dargazany R.. Constitutive behavior and failure prediction of crosslinked polymers exposed to concurrent fatigue and thermal aging: a reduced-order knowledge-driven machine-learned model J. Mater. Sci. 2023 59 5066 84

[4] Tsotsis T. K.,Keller S.,Bardis J.,Bish J.. Preliminary evaluation of the use of elevated pressure to accelerate thermo-oxidative aging in composites Polym. Degrad. Stab. 1999 64 207 12

[5] Ye Y.,Tang G.,Meng X.,Yang R.,Guo B.,Xie X.. Efficient and precise outdoor lifetime prediction method for biodegradable poly (butylene adipate/ terephthalate) under various climate conditions J. Clean. Prod. 2025 500 145276

[6] Wang X.,Petrů M.. Degradation of bending properties of flax fiber reinforced polymer after natural aging and accelerated aging Constr. Build. Mater. 2020 240 117909

[7] Yu H.,Zhang D.,Cui L.,Kong Y.,Jiao X.. A Mach. Learn. approach for efficiently predicting polymer aging from UV-vis spectra J. Phys. Chem. B. 2024 128 9255 61

[8] Sharma H.,Arora G.,Singh M. K..et al. Review of Mach. Learn. approaches for predicting mechanical behavior of composite materials Discov. Appl. Sci. 2025 7 1238

[9] Bernstein R.,Derzon D. K.,Gillen K. T.. Nylon 6.6 accelerated aging studies: thermal-oxidative degradation and its interaction with hydrolysis Polym. Degrad. Stab. 2005 88 480 8

[10] Yarahmadi N.,Vega A.,Jakubowicz I.. Accelerated ageing and degradation characteristics of rigid polyurethane foam Polym. Degrad. Stab. 2017 138 192 200

[11] Richaud E.,Farcas F.,Fayolle B.,Audouin L.,Verdu J.. Accelerated ageing of polypropylene stabilized by phenolic antioxidants under high oxygen pressure J. Appl. Polym. Sci. 2008 110 3313 21

[12] Courvoisier E.,Bicaba Y.,Colin X.. Multi-scale and multi-technical analysis of the thermal degradation of poly(ether imide) Polym. Degrad. Stab. 2018 147 177 86

[13] Courvoisier E.,Bicaba Y.,Colin X.. Multi-scale and multi-technique analysis of the thermal degradation of poly(ether ether ketone) Polym. Degrad. Stab. 2018 151 65 79

[14] Faulkner D. L.. Aging of polypropylene using high oxygen pressure: Influence of sample thickness and stabilization J. Appl. Polym. Sci. 1986 31 2129 38

[15] Zha S.,Lan H.,Lin N.,Meng T.. Degradation and characterization methods for polyethylene gas pipes after natural and accelerated aging Polym. Degrad. Stab. 2023 208 110247

[16] Colin X.,Essatbi F.,Delozanne J.,Moreau G.. A new analytical model for predicting the thermal oxidation kinetics of composite organic m atrices. Application to diamine cross-linked epoxy Polym. Degrad. Stab. 2021 186 109513

[17] Colin X.,Marais C.,Verdu J.. Thermal oxidation kinetics for a poly(bismaleimide) J. Appl. Polym. Sci. 2001 82 3418 30

[18] Omairey E. L.,Zhang Y.,Soenen H.,Carbonneau X.. Parametric analysis and field validations of oxidative ageing in asphalt pavements using multiphysics modelling approaches Int. J. Pavement Eng. 2022 24 2020267

[19] Cassola S.,Duhovic M.,Schmidt T.,May D.. Mach. Learn. for polymer composites process simulation - a review Compos. Part B Eng. 2022 246 110208

[20] Linde E.,Nilsson F.,Barrett M.,Hedenqvist M. S.,Celina M. C.. Time- and feedback-dependent DLO phenomena in oxidative polymer aging Polym. Degrad. Stab. 2021 189 109598

[21] Li X.,Dong C.,Li J.,Bin G.,Zhou C.. Molecular dynamics simulation of aging properties in polymer materials: a review Polym. Bull. 2024 82 1723 53

[22] Fang C.,Wang H.,Zhang Y.,Zhang M.,Shen T.,Du J.. Multi-scale model for the aging performance of particle-filled polymer composites Polymers. 2023 15 PMC10421318

[23] Wang Y.,Wang K.,Zhang C.. Applications of artificial intelligence/machine learning to high-performance composites Compos. Part B Eng. 2024 285 111740

[24] Wang Y.,Lan H.,Meng T.,Chen S.,Zuo J.,Lin N.. A lifetime prediction method of pressured gas polyethylene pipes by thermal-oxidative aging test and tensile test J. Pressure Vessel Technol. 2018 140 011404

[25] Wang Y.,Lan H.,Meng T.. Lifetime prediction of natural gas polyethylene pipes with internal pressures Eng. Fail. Anal. 2019 95 154 63

[26] Ran W.,Niu L.,Wang Y.,Lin N.,Feng G.,Lan H.. A lifetime prediction method of pressured gas polyethylene pipes by strain-hardening modulus and tensile test Appl. Sci. 2023 13 7965

[27] Feng Y.,Hu J.,Peng C.,Wu Q.,Xu Z.. High dual-aging-resistant property obtained in silica fume filled epoxy based composite and its lifetime prediction by aging model Mater. Res. Express 2018 6 016303

[28] Hao Z.,Feng P.,Zhang S.,Zhai Y.. Machine learning for predicting fiber-reinforced polymer durability: a critical review and future directions Compos. Part B Eng. 2025 303 112587

[29] Bernstein R.,Gillen K.. Nylon 6.6 accelerating aging studies: II. Long-term thermal-oxidative and hydrolysis results Polym. Degrad. Stab. 2010 95 1471 9

[30] Xu H.,Tian T.,Zhang J..et al. Hot deformation behavior of the 25CrMo4 steel using a modified arrhenius model Materials. 2022 15 2820 PMC9025652

[31] Celina M.,Gillen K.,Assink R.. Accelerated aging and lifetime prediction: review of non-arrhenius behaviour due to two competing processes Polym. Degrad. Stab. 2005 90 395 404

[32] Ciutacu S.,Budrugeac P.,Niculae I.. Accelerated thermal aging of glass-reinforced epoxy resin under oxygen pressure Polym. Degrad. Stab. 1991 31 365 72

[33] Starkova O.,Gagani A. I.,Karl C. W.,Rocha I. B. C. M.,Burlakovs J.,Krauklis A. E.. Modelling of Environmental Ageing of Polymers and Polymer Composites-Durability Prediction Methods Polymers. 2022 14 907

[34] Xu P.,Ma Y.,Lu W.,Li M.,Zhao W.,Dai Z.. Multi-objective optimization in Mach. Learn. assisted materials design and discovery J. Mater. Inf. 2025 5 26

[35] Nasrin T.,Pourkamali‐anaraki F.,Peterson A. M.. Application of Mach. Learn. in polymer additive manufacturing: a review J. Polym. Sci. 2023 62 2639 69

[36] Das P. P.,Rabby M. M.,Vadlamudi V.,Raihan R.. Moisture content prediction in polymer composites using Mach. Learn. techniques Polymers. 2022 14 4403

[37] Cui C.,Wang H.,Gao X.,Cao G.,Liu Z.. Mach. Learn. model for thickness evolution of oxide scale during hot strip rolling of steels Metall. Mater. Trans. A. 2021 52 4112 24

[38] Gubernatis J. E.,Lookman T.. Mach. Learn. in materials design and discovery: examples from the present and suggestions for the future Phys. Rev. Mater. 2018 2 120301

[39] Taffese W. Z.,Wally G. B.,Magalhães F. C.,Espinosa-leal L.. Concrete aging factor prediction using Mach. Learn Mater. Today Commun. 2024 40 109527

[40] Peng L.,Miao X.,Zhu J..et al. Hybrid Mach. Learn. and multi-objective optimization for intelligent design of green and low-carbon concrete Sustain. Mater. Technol. 2025 45 e01605

[41] Zhu K.,Wu D.,Yang S..et al. Identification of aged polypropylene with Mach. Learn. and near-infrared spectroscopy for improved recycling Polymers. 2025 17 700

[42] Wang J.,Karimi S.,Zeinalzad P.,Zhang J.,Gong Z.. Using Mach. Learn. and experimental study to correlate and predict accelerated aging with natural aging of GFRP composites in hygrothermal conditions Constr. Build. Mater. 2024 438 137264

[43] Nematov D.,Hojamberdiev M.. Mach. Learn.-driven materials discovery: unlocking next-generation functional materials - a review Comput. Condens. Matter 2025 45 e01139

[44] Luo D.,Wang K.,Wang D.,Sharma A.,Li W.,Choi I. H.. Artificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review npj Mater. Sustain. 2025 3 14

[45] Zhang Z.,Wang Y.,Wang W.. Mach. Learn. in gel-based additive manufacturing: from material design to process optimization Gels 2025 11 582

[46] Lundberg S. M.,Lee S. I.. A unified approach to interpreting model predictions. In Advances in neural information processing systems, 2017, 30. https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html. (accessed 2026-04-13)

[47] Lundberg S. M.,Erion G. G.,Lee S. I.. Consistent individualized feature attribution for tree ensembles arXiv 2018 arXiv:1802.03888

[48] Zhong X.,Gallagher B.,Liu S.,Kailkhura B.,Hiszpanski A.,Han T. Y.. Explainable Mach. Learn. in materials science npj Comput. Mater. 2022 8 204

[49] Hu W.,Jing E.,Qiu H.,Sun Z.. Discovering polyimides and their composites with targeted mechanical properties through explainable Mach. Learn J. Mater. Inf. 2025 5 1

[50] Zhu P.,Li Z.,Zhu Y.,Wu Y.,Qu W.. Prediction of the long-term tensile strength of GFRP bars in concrete Buildings 2023 13 1035

[51] Jia N.,Kagan V. A.. Effects of time and temperature on the tension‐tension fatigue behavior of short fiber reinforced polyamides Polym. Compos. 1998 19 408 14

[52] Asteris P. G.,Apostolopoulou M.,Armaghani D. J..et al. On the metaheuristic models for the prediction of cement-metakaolin mortars compressive strength Metaheuristic Comput. Appl. 2020 1 063 99 https://www.techno-press.org/content/?page=article&journal=mca&volume=1&num=1&ordernum=4 (accessed 2026-04-13).

[53] Mouawad F.,Homsi F.,Geara F.,Mina R.. Predicting compressive strength of sustainable concrete using Mach. Learn. and artificial neural networks Constr. Mater. 2025 5 56

[54] Joo C.,Park H.,Kwon H..et al. Mach. Learn. approach to predict physical properties of polypropylene composites: application of MLR, DNN, and random forest to industrial data Polymers. 2022 14

[55] Malashin I, Tynchenko V, Gantimurov A, et al. Boosting-based machine learning applications in polymer science: a review Polymers 2025 17 499 PMC11859903

[56] Loh W. Y.. Classification and regression trees WIREs Data Min. Knowl. Discov. 2011 1 14 23

[57] Breiman L.. Random forests Mach. Learn. 2001 45 5 32

[58] Friedman J. H.. Greedy function approximation: a gradient boosting machine Ann. Statist. 2001 29 1189 232 https://www.jstor.org/stable/2699986 (accessed 2026-04-13).

[59] Chen T.,Guestrin C.. Xgboost: a scalable tree boosting system. In KDD’16: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining; 2016, 785-94

[60] Ke G.,Meng Q.,Finley T..et al. Lightgbm: a highly efficient gradient boosting decision tree. In Advances in neural information processing systems; 2017, 30. https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html. (accessed 2026-04-13)

[61] Dorogush A. V.,Ershov V.,Gulin A.. CatBoost: gradient boosting with categorical features support. arXiv 2018, arXiv:1810.11363

[62] Schölkopf B.,Smola A. J.. Learning with kernels: support vector machines, regularization, optimization, and beyond; MIT press Cambridge, MA, USA.: 2002. https://books.google.com/books?id=y8ORL3DWt4sC&printsec=frontcover&hl=zh-CN#v=onepage&q&f=false (accessed 2026-04-13)

[63] Wu S.,Zhang H.,Wang Y..et al. Concentration prediction of polymer insulation aging indicator-alcohols in oil based on genetic algorithm-optimized support vector machines Polymers. 2022 14 1449 PMC9003247

[64] Drucker H.,Burges C. J. C.,Kaufman L.,Smola A. J.,Vapnik V.. Support vector regression machines. In Advances in neural information processing systems, 1996, 9, 155-61

[65] Cover T.,Hart P.. Nearest neighbor pattern classification IEEE Trans. Inform. Theory 1967 13 21 7

[66] Altman N. S.. An introduction to kernel and nearest-neighbor nonparametric regression Am. Stat. 1992 46 175 85