TY - GEN
T1 - Metal Forming Process Efficiency Improvement Based on AI Services
AU - Boto, Fernando
AU - Cabello, Daniel
AU - Ortega, Juan Antonio
AU - Puigjaner, Blanca
AU - Alonso, Asier
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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. ...
AB - 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. ...
KW - Industry 4.0
KW - Machine Learning
KW - Metal forming
KW - Process data
UR - http://www.scopus.com/inward/record.url?scp=85199151440&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-57496-2_17
DO - 10.1007/978-3-031-57496-2_17
M3 - Conference contribution
AN - SCOPUS:85199151440
SN - 9783031574955
T3 - Lecture Notes in Mechanical Engineering
SP - 167
EP - 176
BT - Advances in Artificial Intelligence in Manufacturing - Proceedings of the 1st European Symposium on Artificial Intelligence in Manufacturing, 2023
A2 - Wagner, Achim
A2 - Alexopoulos, Kosmas
A2 - Makris, Sotiris
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2023
Y2 - 19 September 2023 through 19 September 2023
ER -