| 1. |
- Lopes, Paulo Victor, 1996, et al.
(författare)
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PROCESS MINING AND PRODUCTION ROUTING FAST PROFILING FOR DATA-DRIVEN DIGITAL TWINS
- 2024
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Ingår i: Proceedings - European Council for Modelling and Simulation, ECMS. - 2522-2414. ; 38:1, s. 171-177
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Konferensbidrag (refereegranskat)abstract
- Industry increasingly focuses on Digital Shadows and Twins of production lines, especially for planning, controlling, and optimizing operations. In parallel, shop floor processes can be described using Discrete Event Simulation (DES) models, which are ranked among the top tools for manufacturing system decision support. Although, Process Mining (PM) and model-driven Digital Twins (DT) were investigated in separate research communities. The integration of these two research fields is essential for advancing industrial applications by reducing time and efforts to model and describe processes. Thus, the objective of this paper is to propose a data integration pipeline to enhance realistic event logs and support the early stages of Data-driven Modelling of DT through PM techniques. This paper is expected to provide three relevant contributions. The first contribution is the enhancement of the production system event logs through the implementation of data integration techniques. The second contribution is to enable machine learning techniques to be applied by trace profiling the enhanced event logs, generating an attribute-value database. The third contribution is to extract value from a process-centered analysis, increasing the data value from a practical perspective.
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| 2. |
- Lopes, Paulo Victor, 1996, et al.
(författare)
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Synthetic data generation for digital twins: enabling production systems analysis in the absence of data
- 2024
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Ingår i: International Journal of Computer Integrated Manufacturing. - 0951-192X .- 1362-3052. ; In Press
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Tidskriftsartikel (refereegranskat)abstract
- Industry increasingly focuses on data-driven digital twins of production lines, especially for planning, controlling and optimising applications. However, the lack of open data on manufacturing systems presents a challenge to the development of new data-driven strategies. To fill this gap, the paper aim to introduce a strategy for generating random production lines and simulating their behaviour, thus enabling the generation of synthetic data. So far, such data can be recorded in event logs or machine status format, with the latter adopted for the use cases. To do so, the production lines are modelled using complex network concepts, with the system’s behaviour simulated via an algorithm in Python. Three use cases were assessed, in order to present possible applications. Firstly, the stabilisation of working, starved and blocked machines was investigated until a steady state was reached. The system behaviour was then investigated for different model parameters and simulation intervals. Finally, the production bottleneck behaviour (a phenomenon that can harm the production capacity of manufacturing systems) was statistically studied and described. The authors anticipate that this artificial and parametric data benchmark will enable the development of data-driven techniques without prior need for a real dataset.
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| 3. |
- Yoshiro, Juliano, et al.
(författare)
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HOW TO EVALUATE PROCESS DISCOVERY FOR DIGITAL TWINS IN INDUSTRY 4.0? PROCESS DISCOVERY, HYPOTHESIS TESTING, AND CONFORMANCE ANALYSIS
- 2024
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Ingår i: Proceedings - European Council for Modelling and Simulation, ECMS. - 2522-2414. ; 38:1, s. 178-184
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Konferensbidrag (refereegranskat)abstract
- The field of data science is an emerging area of study that arises in the context of the production of a large volume of data in recent years. The objective of this area is to obtain valuable information that is extracted through data processing. In the industrial context, the identification of failures and bottlenecks in production lines is essential to increase the productivity of the evaluated systems. However, manual analysis can be time-consuming and costly. Process discovery is a set of techniques that includes the use of algorithms to extract a process model from the event log, which can be used as a basis for developing Digital Twins. Therefore, this paper proposes the use of an artificial production line generator so that process mining algorithms can be tested with a large number of samples and different network characteristics. Thus, the main contribution will be the testing of hypotheses to assist in choosing the best algorithms in a practical context.
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