Year: 2024
Type: conference-paper
Abstract: Digital Twins (DTs) provide a suitable solution for manufacturing systems analysis by simulating disruptive scenarios for better decision-making, though their complexity and constant updates remain challenging. Data-Driven Modeling of Digital Twins (D3T) emerges as a promising approach to automate the discovery and updating of these models. This work addresses three key gaps in D3T implementation: data collection, model discovery/validation, and what-if scenario analysis. Finally, proposing the use of artificial data generation, process mining, and automatic simulation techniques to benefit manufacturing systems.
Source: Lirias
Authors: Lopes, Paulo, Lugaresi, Giovanni, Franklin, Rodolfo Pereira, Verri, Filipe
Institutions: Instituto Tecnológico de Aeronáutica, KU Leuven
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7036753284
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