Abstract
In this work, the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in the field of metal forming processes in Shear Forming and Spinning machines are explored. The main objective is to improve the quality of the parts produced and the efficiency of these processes through the implementation of predictive models and online value-added services.
Firstly, different methods for the analysis and evaluation of the quality of manufactured parts are presented. Additionally, predictive models for online failure detection are developed, based on historical and real-time data, which helps prevent failures and reduce production costs.
Furthermore, the challenge of detecting changes in the input material, which can have a significant impact on process outcomes, is addressed.
Lastly, the implementation of an algorithm towards “zero defects” is proposed to achieve optimal conditions in the metal forming process.
The described approaches enable customers of the incremental forming machine manufacturer to access a diverse range of services associated with the implemented methods. ...
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Boto, F., Cabello, D., Ortega, J.A., Puigjaner, B., Alonso, A. (2024). Metal Forming Process Efficiency Improvement Based on AI Services. In: Wagner, A., Alexopoulos, K., Makris, S. (eds) Advances in Artificial Intelligence in Manufacturing. ESAIM 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-57496-2_17
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