Manufacturing Systems Mining: Generation of Real-Time Discrete Event Simulation Models

Lugaresi, Giovanni; Zanotti, Marco; Tarasconi, Diego; Matta, Andrea

doi:10.1109/smc.2019.8914025

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…Each Case ID has machine information passed through the process and also includes the number of productions according to the product. Process mining technology can track each process by classifying and analyzing these Case IDs, and process mining technology is frequently dealt with in recent smart factories, as in [29], in which the authors wanted to predict the manufacturing system, tried to understand the correlation between each manufacturing process through process mining technology, and proved that it could. This paper aims to provide reliable AI results by learning artificial intelligence using event log data and implementing explainable artificial intelligence so that it can adapt to the changing environment more flexibly and autonomously.…”

Section: Event Logmentioning

confidence: 99%

Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

et al. 2022

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Recently, interest in the Cyber-Physical System (CPS) has been increasing in the manufacturing industry environment. Various manufacturing intelligence studies are being conducted to enable faster decision-making through various reliable indicators collected from the manufacturing process. Artificial intelligence (AI) and Machine Learning (ML) have advanced enough to give various possibilities of predicting manufacturing time, which can help implement CPS in manufacturing environments, but it is difficult to secure reliability because it is difficult to understand how AI works, and although it can offer good results, it is often not applied to industries. In this paper, Bidirectional Long Short Term Memory (BI-LSTM) is used to predict process execution time, which is an indicator that can be used as a basis for CPS in the manufacturing process, and the Shapley Additive Explanations (SHAP) algorithm is used to explain how artificial intelligence works. The experimental results of this paper, applying manufacturing data, prove that the results derived from SHAP are effective for workers and AI to collaborate.

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Section: Event Logmentioning

confidence: 99%

Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

et al. 2022

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“…Process mining is currently used for many purposes, and although it has been frequently implemented in business processes, research using process mining technology in manufacturing processes has become more popular recently. In [22], the author explained the relationship between each activity in the system model, and used this to predict future activities based on previous activities by estimating the level of detail of the system model. In general, process mining attempts to predict a process by considering the process's past events and the sequence of subsequent events.…”

Section: Log Data Analysismentioning

confidence: 99%

A Novel Embedding Model Based on a Transition System for Building Industry-Collaborative Digital Twin

et al. 2022

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Recently, the production environment has been rapidly changing, and accordingly, correct mid term and short term decision-making for production is considered more important. Reliable indicators are required for correct decision-making, and the manufacturing cycle time plays an important role in manufacturing. A method using digital twin technology is being studied to implement accurate prediction, and an approach utilizing process discovery was recently proposed. This paper proposes a digital twin discovery framework using process transition technology. The generated digital twin will unearth its characteristics in the event log. The proposed method was applied to actual manufacturing data, and the experimental results demonstrate that the proposed method is effective at discovering digital twins.

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“…Notice that also simulation graphs (i.e., event relationship graphs) can be obtained, for instance exploiting direct conversion from Petri Nets [43]. Further details on model generation and conversion steps are available in related works [44,45].…”

Section: Model Generationmentioning

confidence: 99%

Automated manufacturing system discovery and digital twin generation

Lugaresi

Matta

2021

Journal of Manufacturing Systems

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Manufacturing Systems Mining: Generation of Real-Time Discrete Event Simulation Models

Cited by 6 publications

References 24 publications

Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

A Novel Embedding Model Based on a Transition System for Building Industry-Collaborative Digital Twin

Automated manufacturing system discovery and digital twin generation

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