Coşkun, YusufKoçak, ZakirAkgüngör, ErenÖzyılmaz, LaleÖzyılmaz, Yakup Hakan2026-07-102026-07-102026Coşkun, Y., Koçak, Z., Akgüngör, E., Özyılmaz, L., & Özyılmaz, Y. H. (2026). Symmetry-breaking and fault-tolerance analysis of a twelve-legged jansen robot using a hybrid FEA-ANFIS framework. Symmetry, 18(7), pp. 1-37. https://doi.org/10.3390/sym180710682073-8994https://doi.org/10.3390/sym18071068https://hdl.handle.net/20.500.13055/1530This study presents a comprehensive symmetry-breaking analysis framework for a twelve legged Jansen walking robot, integrating finite element analysis (FEA) with adaptive neuro fuzzy inference system (ANFIS) surrogate modeling. A systematic dataset of 210 cases was generated by combining 21 single- and multi-leg failure scenarios across 10 load levels (20–200 N) on the PLA-based 3D-printed prototype. Two novel dimensionless metrics are introduced: the Resilience Index (RI), quantifying the proportional stress increase relative to the baseline, and the Asymmetry Index (AI), measuring leg-reaction force distribution imbalance. Results identify a clear fault-tolerance threshold between two- and four-leg failures: single-leg failures remain at LOW risk (RI < 0.20), while three-leg asymmetric failures (S18) reach CRITICAL level (RI = 1.13, ~97% of PLA yield strength). A hybrid machine learning framework is proposed, applying ANFIS to maximum stress (R2 = 0.817) and safety factor (R2 = 0.936) predictions, while reserving FEA tables for bimodal out puts. The ANFIS surrogate achieves approximately 106× speedup over FEA (262.6 µs vs. 5–8 min), enabling real-time fault diagnosis and digital twin applications. The framework is generalizable to other multi-legged robotic systems requiring fault-tolerance evaluation.eninfo:eu-repo/semantics/openAccessMulti-Legged RobotJansen MechanismSymmetry BreakingFault ToleranceFinite Element AnalysisANFISSurrogate ModelDigital TwinArticle10.3390/sym18071068187137Q2WOS:0018332522000012-s2.0-105045909912Q1