Sala, Siva TejaSiva TejaSalaKörner, RichardRichardKörnerHuber, NorbertNorbertHuberKašaev, NikolaiNikolaiKašaev2023-10-262023-10-262023-11-01Manufacturing Letters 38: 60-64 (2023-11-01)https://hdl.handle.net/11420/43839Laser peen forming uses laser-pulse-induced strains to deform sheets by adjusting laser parameters and peening patterns. Finding an optimal pattern in a vast space of practically infinite solutions is challenging. This study presents a workflow using a simplified model to predict deformation. A machine learning-based cellular automata neural network (CANN) and genetic algorithm (GA) were used for pattern prediction. Experiments showed high process uncertainty, justifying simplified modeling. The CANN predicted patterns reliably but lacked generalization due to insufficient deformation data for various process parameters. The GA required optimization efforts to reduce computation time but was successful at generalizing pattern prediction.en#PLACEHOLDER_PARENT_METADATA_VALUE#20236064Elsevierhttps://creativecommons.org/licenses/by/4.0/Cellular automata neural networkgenetic algorithmlaser peen formingmachine learningEngineering and Applied OperationsJournal Article10.15480/882.876410.1016/j.mfglet.2023.09.00610.15480/882.8764Journal Article