20.500.12556/DKUM-91520 Optimizing laser cutting of stainless steel using latin hypercube sampling and neural networks Optimizing cutting parameters in fiber laser cutting of austenitic stainless steel is challenging due to the complex interplay of multiple variables and quality metrics. To solve this problem, Latin hypercube sampling was used to ensure a comprehensive and efficient exploration of the parameter space with a smaller number of trials (185), coupled with feedforward neural networks for predictive modeling. The networks were trained with a leave-oneout cross-validation strategy to mitigate overfitting. Different configurations of hidden layers, neurons, and training functions were used. The approach was focused on minimizing dross and roughness on both the top and bottom areas of the cut surfaces. During the testing phase, an average MSE of 0.063 and an average MAPE of 4.68% were achieved by the models. Additionally, an experimental test was performed on the best parameter settings predicted by the models. Initial modelling was conducted for each quality metric individually, resulting in an average percentage difference of 1.37% between predicted and actual results. Grid search was also per formed to determine an optimal input parameter set for all outputs, with predictions achieving an average ac curacy of 98.34%. Experimental validation confirmed the accuracy and robustness of the model predictions, demonstrating the effectiveness of the methodology in optimizing multiple parameters of complex laser cutting processes. laser cutting optimization cut surface quality dross formation Latin hypercube sampling feedforward neural network optimizacija laserskega rezanja kakovost rezalne površine nastajanje žlindre vzorčenje latinske hiperkocke napredne nevronske mreže true false true Elsevier Angleški jezik Slovenski jezik https://www.sciencedirect.com/journal/optics-and-laser-technology © 2024 The Author(s) Članek v reviji 2025-01-10 15:00:44 2025-01-10 15:00:44 2026-01-19 14:30:35 0000-00-00 00:00:00 2025 0 0 str. 1-10 pt. B, [article no.] 112220 Vol. 182 Apr. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-08-20 2024-11-24 2024-11-30 621.9:004.8 220295427 10.1016/j.optlastec.2024.112220 0030-3992 1-s2.0-S0030399224016785-main.pdf 1-s2.0-S0030399224016785-main.pdf 1 35F8348F05CFAF548C77214BAB0D8A38 c28e1f2452e420c92442977c4530c24b99f46d4b094c0983f965a9edb7d807a8 ba4ba2d7-cf5b-11ef-8532-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=187740 https://www.sciencedirect.com/science/article/pii/S0030399224016785?via%3Dihub 0 c54debe8-cf5b-11ef-8532-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=187742 Fakulteta za strojništvo 0 0 0