Sala, Siva TejaSiva TejaSalaBock, Frederic E.Frederic E.BockPöltl, DominikDominikPöltlKlusemann, BenjaminBenjaminKlusemannHuber, NorbertNorbertHuberKašaev, NikolaiNikolaiKašaev2023-12-142023-12-142023Journal of Intelligent Manufacturing (in Press): (2023)https://hdl.handle.net/11420/44628Abstract: The precise bending of sheet metal structures is crucial in various industrial and scientific applications, whether to modify deformation in an existing component or to achieve specific shapes. Laser peen forming (LPF) is proven as an innovative forming process for sheet metal applications. LPF involves inducing mechanical shock waves into a specimen that deforms the affected region to a certain desired curvature. The degree of deformation induced after LPF depends on numerous experimental factors such as laser energy, the number of peening sequences, and the thickness of the specimen. Consequently, comprehending the complex dependencies and selecting the appropriate set of LPF process parameters for application as a forming or correction process is crucial. The main objective of the present work is the development of a data-driven approach to predict the deformation obtained from LPF for various process parameters. Artificial neural network (ANN) was trained, validated, and tested based on experimental data. The deformation obtained from LPF is successfully predicted by the trained ANN. A novel process planning approach is developed to demonstrate the usability of ANN predictions to obtain the desired deformation in a treated region. The successful application of this approach is demonstrated on three benchmark cases for thin Ti-6Al-4V sheets, such as deformation in one direction, bi-directional deformation, and modification of an existing deformation in pre-bent specimens via LPF. Graphical abstract: [Figure not available: see fulltext.].en0956-55152023https://creativecommons.org/licenses/by/4.0/Artificial neural networksDimensional analysisLaser peen forming (LPF)Machine learningProcess planningMLE@TUHHEngineering and Applied OperationsJournal Article10.15480/882.893910.1007/s10845-023-02240-y10.15480/882.8939Journal Article