Reliability oriented degradation modeling of fused filament fabrication printed acrylonitrile butadiene styrene components under cyclic mechanical loads using explainable AI techniques
1
Isparta Applied Science Universty, Turkey
Submission date: 2025-12-15
Final revision date: 2026-02-12
Acceptance date: 2026-02-19
Online publication date: 2026-03-03
KEYWORDS
additive manufacturingmachine learningfatigue life predictionFused Filament Fabrication (FFF)ABS polymers
TOPICS
ABSTRACT
The fatigue performance of Fused Filament Fabrication (FFF) printed acrylonitrile butadiene styrene (ABS) is critically governed by process-induced anisotropy, yet the integration of reliability analysis with explainable machine learning remains unexplored. This study presents a reliability-oriented Explainable Artificial Intelligence (XAI) framework to model fatigue degradation in FFF fabricated ABS. An empirical dataset was generated by testing 150 specimens with systematically varied printing parameters. Reliability analysis using a two-parameter Weibull distribution yielded a shape parameter (β) of 2.48, confirming a distinct wear-out failure mode. Among the tested algorithms, Gradient Boosting achieved the highest predictive accuracy (R²= 0.90). Explainability analyses via SHAP and LIME revealed that build orientation and layer height are the dominant factors influencing fatigue life, aligning with physical degradation mechanisms. This framework offers a transparent tool for fatigue life prediction and process optimization in additive manufacturing.
REFERENCES (37)
1.
Gibson I, Rosen D, Stucker B. Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing (3rd ed.). Springer: 2021.
2.
Ngo T D, Kashani A, Imbalzano G, Nguyen K T Q, Hui D. Additive manufacturing (3D printing): A review of materials, methods, applications and challenges. Composites Part B: Engineering 2018; 143: 172–196. https://doi.org/10.1016/j.comp....
3.
Dizon J R C, Espera A H, Chen Q, Advincula R C. Mechanical characterization of 3D-printed polymers. Additive Manufacturing 2018; 20: 44–67. https://doi.org/10.1016/j.addm....
4.
Charalampous, P., Kladovasilakis, N., Kostavelis, I. et al. Machine Learning-Based Mechanical Behavior Optimization of 3D Print Constructs Manufactured Via the FFF Process. J. of Materi Eng and Perform 31, 4697–4706 (2022). https://doi.org/10.1007/s11665....
5.
Malashin, I., Martysyuk, D., Tynchenko, V., Gantimurov, A., Nelyub, V., & Borodulin, A. Data-Driven Optimization of Discontinuous and Continuous Fiber Composite Processes Using Machine Learning: A Review. Polymers, 2025, 17(18), 2557. https://doi.org/10.3390/polym1....
6.
Raj, T., Tiwary, A., Jain, A. et al. Machine learning-assisted prediction modeling for anisotropic flexural strength variations in fused filament fabrication of graphene reinforced poly-lactic acid composites. Prog Addit Manuf 10, 2585–2599 (2025). https://doi.org/10.1007/s40964....
7.
Yu J, Yao D, Wang L, Xu M. Machine Learning in Predicting and Optimizing Polymer Printability for 3D Bioprinting. Polymers. 2025; 17(13):1873. https://doi.org/10.3390/polym1....
8.
Espino M T, Tuazon B J, Espera A H, Nocheseda C J C, Manalang R S, Dizon J R C, Advincula R C. Statistical methods for design and testing of 3D-printed polymers. MRS Communications 2023; 13(2): 193-211. https://doi.org/10.1557/s43579....
9.
Alkunte S, Fidan I. Machine Learning-Based Fatigue Life Prediction of Functionally Graded Materials Using Material Extrusion Technology. Journal of Composites Science 2023; 7(10): 420. https://doi.org/10.3390/jcs710....
10.
Çiçek U. Additive manufacturing parameters significantly influence both mechanical toughness and stab resistance in 3D printed polycarbonate armor structures. Polymers 2025; 17(19): 2699. https://doi.org/10.3390/polym1....
11.
Nasrin T, Pourkamali Anaraki F, Peterson A M. This review synthesizes research on machine learning applications in polymer additive manufacturing. Journal of Polymer Science 2024; 62(12): 2639–2669. https://doi.org/10.1002/pol.20....
12.
Ukwaththa J, Herath S, Meddage D P P. A review of machine learning (ML) and explainable AI methods in additive manufacturing. Materials Today Communications 2024; 41: 110294. https://doi.org/10.1016/j.mtco....
13.
Boppana C, Ali F. Improvement of tensile strength of fused deposition modelling (FDM) part using ANN and genetic algorithm techniques. International Journal of Industrial Engineering and Operations Management 2024; 6(2): 117–142. https://doi.org/10.1108/IJIEOM....
14.
Ahn S H, Montero M, Odell D, Roundy S, Wright P K. Anisotropic material properties of fused deposition modeling ABS. Rapid Prototyping Journal 2002; 8(4): 248–257. https://doi.org/10.1108/135525....
15.
Cruz Sanchez F A, Boudaoud H, Muller L, Camargo M. Towards a standard experimental protocol for open source additive manufacturing. Virtual and Physical Prototyping 2014; 9. 10.1080/17452759.2014.919553.
16.
Arif M F, Kumar S, Varadarajan K M. Cantwell W J. Performance of biocompatible PEEK processed by fused deposition additive manufacturing. Materials and Design 2018; 146: 249–259. doi: 10.1016/j.matdes.2018.03.015.
17.
Lanzotti A, Martorelli M, Staiano G. The impact of process parameters on mechanical properties of parts fabricated in PLA with an open-source 3-D printer. Rapid Prototyping Journal 2015; 21(5): 604–617. 10.1108/RPJ-09-2014-0135.
18.
Bakhtiari, H., Aamir, M., & Tolouei-Rad, M. Effect of 3d printing parameters on the fatigue properties of parts manufactured by fused filament fabrication: A review. Applied Sciences,2023, 13(2), Article 904. https://doi.org/10.3390/ app13020904
19.
Gardner J M, O’Donnell K A, Chawla N. Fatigue and fracture behavior of additively manufactured polymers. Materials Science and Engineering A 2020; 781: 139228. https://doi.org/10.3390/books9....
20.
Safai L, Cuellar Lopez J, Smit G, Zadpoor A A. A review of the fatigue behavior of 3D printed polymers. Additive Manufacturing 2019; 28: 87–97. https://doi.org/10.1016/j.addm....
21.
Lundberg S M, Lee S I. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems (NeurIPS) 2017; 30: 4765–4774. 10.48550/arXiv.1705.07874
22.
Ribeiro M T, Singh S, Guestrin C. "Why should I trust you?": Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD Conference 2016: 1135–1144. https://doi.org/10.1145/293967....
23.
ASTM. ASTM D638 – Standard test method for tensile properties of plastics. ASTM International: 2023.
24.
He F, Wu W, Li X, Liu T. Effects of printing parameters on the fatigue behaviour of fused deposition modelling printed ABS polymers. Materials 2021; 14(13): 3726. https://doi.org/10.3390/ma1413....
25.
ASTM. ASTM D7774 – Standard practice for conditioning plastics for testing. ASTM International: 2022.
26.
Domingo-Espin M, Travieso-Rodriguez J A, Jerez-Mesa R, Lluma-Fuentes J. Fatigue Performance of ABS Specimens Obtained by Fused Filament Fabrication. Materials 2018; 11(12): 2521. https://doi.org/10.3390/ma1112....
27.
Caminero M A, Chacón J M, García-Moreno I, Rodríguez G P. Impact damage resistance of 3D printed continuous fibre reinforced thermoplastic composites using fused deposition modelling. Composites Part B 2018; 148: 93–103. https://doi.org/10.1016/j.comp....
28.
Strzelecki P, Sempruch J. Determination of the Statistical Power of Fatigue Characteristics in Relation to the Number of Samples. Applied Sciences. 2024; 14(6):2440. https://doi.org/10.3390/app140....
29.
Doshi M. Printing parameters and materials affecting mechanical properties of FDM parts. Journal of Manufacturing Processes 2022; 78: 74–90. https://doi.org/10.1016/j.jmap....
30.
Zhan Z. Li H. Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L. International Journal of Fatigue 2021; 142. 105941. 10.1016/j.ijfatigue.2020.105941.
32.
Sezer H K, Eren O, Yıldız A R. Statistical fatigue life assessment of additively manufactured polymers using Weibull distribution. Polymer Testing 2021; 96: 107084. https://doi.org/10.1016/j.poly....
33.
Akbari P, Zamani M. Mostafaei A. Machine learning prediction of mechanical properties in metal additive manufacturing. Additive Manufacturing 2024; 91: 104320. 10.1016/j.addma.2024.104320.
34.
Hastie T, Tibshirani R, Friedman J. The elements of statistical learning (2nd ed.). Springer: 2021.
35.
Afrose M F, Masood S H, Nikzad M, Sbarski I. Effects of part build orientations on fatigue behaviour of FDM-processed PLA material. Progress in Additive Manufacturing 2016; 1(1–2): 21–28. https://doi.org/10.1007/s40964....
36.
Shanmugam V, Das O, Babu N B K, Marimuthu U, Veerasimman A, Deepak Joel Johnson R, Neisiany R, Hedenqvist M, Ramakrishna S, Berto F. Fatigue behaviour of FDM-3D printed polymers, polymeric composites and architected cellular materials. International Journal of Fatigue 2021; 143: 106007. 10.1016/j.ijfatigue.2020.106007
37.
Yankin A, Serik G, Danenova S, Alipov Y, Temirgali A, Talamona D, Perveen A. Optimization of Fatigue Performance of FDM ABS and Nylon Printed Parts. Micromachines 2023; 14(2): 304. https://doi.org/10.3390/mi1402....
Share
RELATED ARTICLE
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
