TY - GEN
T1 - Decision Support System (DSS) for Manufacturing Engineering of Cans Rolling
AU - Martín, Ander
AU - Penalva, Mariluz
AU - Veiga, Fernando
AU - Ruiz, Cristina
AU - Martínez, Víctor
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025
Y1 - 2025
N2 - Decision Support Systems (DSS) can help factory workers in the decision-making step of multiple tasks. In digital factories, these systems make use of data towards a human-centered manufacturing. Rolling of large and thick plates into cans is a common practice in the metal forming industry to fabricate pipes or tanks. The process is adjusted by trial and error with a high level of operator intervention. Furthermore, only a small number of cans are identical. The objective of this work is to prescribe, by means of a DSS, the process parameters to be applied by the operator in the machine to optimize the can fabrication. The development of the DSS involved several steps, including firstly signal preprocessing and classification and then data extraction, aggregation, and regression in a multi-stage prediction framework. A significant use of domain knowledge for a data-centric solution contributes to the quality of the recommendations and the ability to organize and transfer know-how among operators.
AB - Decision Support Systems (DSS) can help factory workers in the decision-making step of multiple tasks. In digital factories, these systems make use of data towards a human-centered manufacturing. Rolling of large and thick plates into cans is a common practice in the metal forming industry to fabricate pipes or tanks. The process is adjusted by trial and error with a high level of operator intervention. Furthermore, only a small number of cans are identical. The objective of this work is to prescribe, by means of a DSS, the process parameters to be applied by the operator in the machine to optimize the can fabrication. The development of the DSS involved several steps, including firstly signal preprocessing and classification and then data extraction, aggregation, and regression in a multi-stage prediction framework. A significant use of domain knowledge for a data-centric solution contributes to the quality of the recommendations and the ability to organize and transfer know-how among operators.
KW - classification
KW - data aggregation
KW - data-centric regression
KW - Decision Support System
KW - domain knowledge-based feature extraction
KW - machine learning
KW - metal forming
UR - http://www.scopus.com/inward/record.url?scp=105001376517&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-86489-6_18
DO - 10.1007/978-3-031-86489-6_18
M3 - Conference contribution
AN - SCOPUS:105001376517
SN - 9783031864889
T3 - Lecture Notes in Mechanical Engineering
SP - 171
EP - 179
BT - Advances in Artificial Intelligence in Manufacturing II - Proceedings of the 2nd European Symposium on Artificial Intelligence in Manufacturing, 2024
A2 - Alexopoulos, Kosmas
A2 - Makris, Sotiris
A2 - Stavropoulos, Panagiotis
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2024
Y2 - 16 October 2024 through 16 October 2024
ER -
