TY - GEN
T1 - KI-Net: AI-Based Optimization in Industrial Manufacturing—A Project Overview
AU - Freudenthaler, B.
AU - Martinez-Gil, J.
AU - Fensel, A.
AU - Höfig, K.
AU - Huber, S.
AU - Jacob, D.
N1 - Conference code: 291639
Export Date: 14 December 2023
Correspondence Address: Freudenthaler, B.; Software Competence Center Hagenberg GmbH, Softwarepark 32a, Austria; email: [email protected]
Funding details: AB292
Funding details: Bundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und Technologie, BMK
Funding details: Österreichische Forschungsförderungsgesellschaft, FFG
Funding details: Bundesministerium für Digitalisierung und Wirtschaftsstandort, BMDW
Funding text 1: Acknowledgements. The research reported in this paper has been funded by the European Interreg Austria-Bavaria project ”KI-Net (AB292)”. It has also been partly funded by BMK, BMDW, and the State of Upper Austria in the frame of the COMET Program managed by FFG.
References: Alam, M., Fensel, A., Martinez-Gil, J., Moser, B., Recupero, D.R., Sack, H., Special issue on machine learning and knowledge graphs (2022) Future Gener. Comput. Syst., 129, pp. 50-53; Bayeff-Filloff, F., Stecher, D., Höfig, K., Stepwise sample generation (2022) Proceedings of the 18Th International Confernce on Computer Aided Syposium Theory (EUROCAST 2022), , Las Palmas de Gran Canaria, Spain; Buchgeher, G., Gabauer, D., Martinez-Gil, J., Ehrlinger, L., Knowledge graphs in manufacturing and production: A systematic literature review (2021) IEEE Access, 9, pp. 55537-55554; Huber, S., Waclawek, H., Ck-continuous spline approximation with tensorflow gradient descent optimizers (2022) Proceedings 18Th International Confernce on Comp. Aided Sys. Theory (EUROCAST 2022), , Las Palmas de Gran Canaria, Spain; Kainzner, M., Klösch, C., Filipiak, D., Chhetri, T.R., Fensel, A., Martinez-Gil, J., Towards reusable ontology alignment for manufacturing maintenance (2021) Tiddi, I., Maleshkova, M., Pellegrini, T., De Boer, V., (Eds.) Joint Proceedings of the Semantics Co-Located Events: Poster& Demo Track and Workshop on Ontology-Driven Conceptual Modelling of Digital Twins Co-Located with Semantics 2021, Amsterdam and Online, September 6–9, 2021, Volume 2941 of CEUR Workshop Proceedings. Ceur-Ws.Org; Lehenauer, M., Wintersteller, S., Uray, M., Huber, S., Improvements for mlrose applied to the traveling salesman problem (2022) Proceedings of the 18Th International Conference on Computer Aided System Theory (EUROCAST 2022), , Las Palmas de Gran Canaria, Spain; Mahmoud, S., Martinez-Gil, J., Praher, P., Freudenthaler, B., Girkinger, A., Deep learning rule for efficient changepoint detection in the presence of non-linear trends (2021) DEXA 2021. CCIS, 1479, pp. 184-191. , https://doi.org/10.1007/978-3-030-87101-7 18, Kotsis, G., et al. (eds.), vol., pp. , Springer, Cham; Martinez-Gil, J., Buchgeher, G., Gabauer, D., Freudenthaler, B., Filipiak, D., Fensel, A., Root cause analysis in the industrial domain using knowledge graphs: A case study on power transformers (2021) Longo, F., Affenzeller, M., Padovano, A. (Eds.) Proceedings of the 3Rd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2022), Virtual Event/Upper Austria University of Applied Sciences-Hagenberg Campus-Linz, Austria, 17–19 November 2021, Volume 200 of Procedia Computer Science, Pp. 944–953. Elsevier; Schäfer, G., Kozlica, R., Wegenkittl, S., Huber, S., An architecture for deploying reinforcement learning in industrial environments (2022) Proceedings of the 18Th International Conference on Computer Aided System Theory (EUROCAST, p. 2022. , Las Palmas de Gran Canaria, Spain, Feb
PY - 2022
Y1 - 2022
N2 - Artificial intelligence (AI) is a crucial technology of industrial digitalization. Especially in the production industry, a great potential is present in optimizing existing processes, e.g., concerning resource consumption, emission reduction, process and product quality improvements, predictive maintenance, and so on. Some of this potential is addressed by methods of industrial analytics beyond specific production technology. Furthermore, particular technological aspects in production systems address another part of this potential, e.g., mechatronics, robotics and motion control, automation systems, and so on. The problem is that the field of AI includes many research areas and methods, and many companies are losing the overview of the necessary and appropriate methods for solving the company problems. The reasons for this are, on the one hand, a lack of expertise in AI and, on the other hand, high complexity and risks of use for the companies (especially for SMEs). As a result, many potentials cannot yet be exploited. The KI-NET project aims to fill this gap, whereby a project overview is presented in this contribution. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
AB - Artificial intelligence (AI) is a crucial technology of industrial digitalization. Especially in the production industry, a great potential is present in optimizing existing processes, e.g., concerning resource consumption, emission reduction, process and product quality improvements, predictive maintenance, and so on. Some of this potential is addressed by methods of industrial analytics beyond specific production technology. Furthermore, particular technological aspects in production systems address another part of this potential, e.g., mechatronics, robotics and motion control, automation systems, and so on. The problem is that the field of AI includes many research areas and methods, and many companies are losing the overview of the necessary and appropriate methods for solving the company problems. The reasons for this are, on the one hand, a lack of expertise in AI and, on the other hand, high complexity and risks of use for the companies (especially for SMEs). As a result, many potentials cannot yet be exploited. The KI-NET project aims to fill this gap, whereby a project overview is presented in this contribution. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
KW - Artificial intelligence
KW - Digital twin
KW - Knowledge graphs
KW - Manufacturing
KW - Robotics
KW - Systems engineering
KW - Emission control
KW - Knowledge graph
KW - Crucial technology
KW - Emission process
KW - Emission reduction
KW - Industrial manufacturing
KW - Optimisations
KW - Production industries
KW - Reduction process
KW - Resources consumption
U2 - 10.1007/978-3-031-25312-6_65
DO - 10.1007/978-3-031-25312-6_65
M3 - Conference contribution
SN - 978-3-031-25311-9
VL - 13789 LNCS
T3 - Lecture Notes in Computer Science
BT - Computer Aided Systems Theory – EUROCAST 2022
PB - Springer Nature
T2 - 18th International Conference on Computer Aided Systems Theory, EUROCAST 2022
Y2 - 20 February 2022 through 25 February 2022
ER -