20.500.12556/RUL-186412 Surrogate model for FEA analysis and damage calculation used for exhaust system validation A surrogate neural network model for fatigue assessment and optimisation of an exhaust system is presented in this study. The approach is based on established fatigue analysis tools and employs a parameterised sample geometry. Conventional numerical methods were employed to generate a sufficiently large sample set and to provide stress field data. Fatigue damage calculations were subsequently performed as a post-processing step to prepare input data for the surrogate model. The resulting surrogate model is capable of predicting both the maximum fatigue damage value and its spatial location directly from geometric parameters. Due to the complexity of the prediction task and the wide range of damage values, extensive effort was devoted to model optimisation. Proprietary data pre-processing techniques proved essential for effective neural network training, and the network hyperparameters were tuned to achieve satisfactory predictive performance. To further address the wide range of damage values, A logarithmic transformation with inverse transformation correction was utilised. As sample generation is computationally expensive, the influence of sample size on prediction accuracy was also investigated. The proposed surrogate methodology enables efficient fatigue assessment and is suitable for iterative design and optimisation workflows. surrogate model fatigue damage calculation exhaust systems critical plane approach neural networks nadomestni modeli utrujanje izračun škode izpušni sistemi metoda kritične ravnine nevronske mreže true false true Angleški jezik Slovenski jezik Članek v reviji 2026-09-01 10:44:39 2026-09-01 10:44:50 2026-09-03 04:37:59 0000-00-00 00:00:00 2026 0 0 19 str. Vol. 32, art. 112478 2026 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 621.43.06:519.6 2590-1230 10.1016/j.rineng.2026.112478 289570051 Zaletel...Surrogate_model....pdf Zaletel...Surrogate_model....pdf 1 DEF7541FE652B40091FD403054AF14A4 74fd4af71f45c27080b34812e44414bbd902a617a9bda2c83d86e6420166df1f 3b49c7b2-a5e1-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=245656 https://www.sciencedirect.com/science/article/pii/S259012302603495X 1 fc3b6ca3-a5e0-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=245654 Fakulteta za strojništvo 0 0 0