Decision Support System (DSS) for Manufacturing Engineering of Cans Rolling

Ander Martín; Mariluz Penalva; Fernando Veiga; Cristina Ruiz; Víctor Martínez

doi:10.1007/978-3-031-86489-6_18

Decision Support System (DSS) for Manufacturing Engineering of Cans Rolling

Ander Martín^*
, Mariluz Penalva
, Fernando Veiga
, Cristina Ruiz
, Víctor Martínez

^*Corresponding author for this work

Smart manufacturing technologies

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Advances in Artificial Intelligence in Manufacturing II - Proceedings of the 2nd European Symposium on Artificial Intelligence in Manufacturing, 2024
Editors	Kosmas Alexopoulos, Sotiris Makris, Panagiotis Stavropoulos
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	171-179
Number of pages	9
ISBN (Print)	9783031864889
DOIs	https://doi.org/10.1007/978-3-031-86489-6_18
Publication status	Published - 2025
Event	2nd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2024 - Athens, Greece Duration: 16 Oct 2024 → 16 Oct 2024

Publication series

Name	Lecture Notes in Mechanical Engineering
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Conference

Conference	2nd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2024
Country/Territory	Greece
City	Athens
Period	16/10/24 → 16/10/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Decision Support System
classification
data aggregation
data-centric regression
domain knowledge-based feature extraction
machine learning
metal forming

Access to Document

10.1007/978-3-031-86489-6_18

Cite this

Martín, A., Penalva, M., Veiga, F., Ruiz, C., & Martínez, V. (2025). Decision Support System (DSS) for Manufacturing Engineering of Cans Rolling. In K. Alexopoulos, S. Makris, & P. Stavropoulos (Eds.), Advances in Artificial Intelligence in Manufacturing II - Proceedings of the 2nd European Symposium on Artificial Intelligence in Manufacturing, 2024 (pp. 171-179). (Lecture Notes in Mechanical Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-86489-6_18

Martín, Ander ; Penalva, Mariluz ; Veiga, Fernando et al. / Decision Support System (DSS) for Manufacturing Engineering of Cans Rolling. Advances in Artificial Intelligence in Manufacturing II - Proceedings of the 2nd European Symposium on Artificial Intelligence in Manufacturing, 2024. editor / Kosmas Alexopoulos ; Sotiris Makris ; Panagiotis Stavropoulos. Springer Science and Business Media Deutschland GmbH, 2025. pp. 171-179 (Lecture Notes in Mechanical Engineering).

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abstract = "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.",

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author = "Ander Mart{\'i}n and Mariluz Penalva and Fernando Veiga and Cristina Ruiz and V{\'i}ctor Mart{\'i}nez",

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Martín, A, Penalva, M, Veiga, F, Ruiz, C & Martínez, V 2025, Decision Support System (DSS) for Manufacturing Engineering of Cans Rolling. in K Alexopoulos, S Makris & P Stavropoulos (eds), Advances in Artificial Intelligence in Manufacturing II - Proceedings of the 2nd European Symposium on Artificial Intelligence in Manufacturing, 2024. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 171-179, 2nd European Symposium on Artificial Intelligence in Manufacturing, ESAIM 2024, Athens, Greece, 16/10/24. https://doi.org/10.1007/978-3-031-86489-6_18

Decision Support System (DSS) for Manufacturing Engineering of Cans Rolling. / Martín, Ander; Penalva, Mariluz; Veiga, Fernando et al.
Advances in Artificial Intelligence in Manufacturing II - Proceedings of the 2nd European Symposium on Artificial Intelligence in Manufacturing, 2024. ed. / Kosmas Alexopoulos; Sotiris Makris; Panagiotis Stavropoulos. Springer Science and Business Media Deutschland GmbH, 2025. p. 171-179 (Lecture Notes in Mechanical Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 - Decision Support System

KW - classification

KW - data aggregation

KW - data-centric regression

KW - domain knowledge-based feature extraction

KW - machine learning

KW - metal forming

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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 -

Martín A, Penalva M, Veiga F, Ruiz C, Martínez V. Decision Support System (DSS) for Manufacturing Engineering of Cans Rolling. In Alexopoulos K, Makris S, Stavropoulos P, editors, Advances in Artificial Intelligence in Manufacturing II - Proceedings of the 2nd European Symposium on Artificial Intelligence in Manufacturing, 2024. Springer Science and Business Media Deutschland GmbH. 2025. p. 171-179. (Lecture Notes in Mechanical Engineering). doi: 10.1007/978-3-031-86489-6_18

Decision Support System (DSS) for Manufacturing Engineering of Cans Rolling

Abstract

Publication series

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UN SDGs

Keywords

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