Automated manufacturing system discovery and digital twin generation

Lugaresi, Giovanni; Matta, Andréa

doi:10.1016/j.jmsy.2021.01.005

Cited by 80 publications

(26 citation statements)

References 43 publications

(58 reference statements)

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“…Ref. [19] proposed a method for automatically discovering manufacturing systems and creating digital twins based on process mining. The author applied the proposed method to the actual manufacturing line and proved that the results were effective.…”

Section: Log Data Analysismentioning

confidence: 99%

“…Each process is finally terminated with QC2. The following process is a relatively simple process, but since a smart factory process is very complex, it may be difficult to train an artificial intelligence neural network [19]. This is because it can interfere with the creation of a valid digital twin with repeated activities that interfere, making it difficult to understand.…”

Section: Real World Datasetmentioning

confidence: 99%

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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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Section: Log Data Analysismentioning

confidence: 99%

Section: Real World Datasetmentioning

confidence: 99%

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

et al. 2022

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“…Üretim ortamlarında, özellikle yüksek verimlilik için tasarım, montaj, işleme ve lojistik gibi aşamalarda dijital ikizden faydalanılmaktadır. Dijital ikizler doğru ve etkili bir şekilde kullanıldığında, üretim süreçlerini daha esnek, uyarlanabilir ve öngörülebilir hale getirmektedir (Lugaresi ve Matta, 2021). Araştırmacılar tarafından, üretim süreçlerinde dijital ikiz teknolojisinin kullanımına dair dört kategori tanımlanmıştır.…”

Section: üReti̇m Sektöründe Di̇ji̇tal İki̇z Uygulamalariunclassified

A New Productivity Technology for Business: Digital Twin

Duman

2022

Verimlilik Dergisi

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Bu makalenin amacı, Endüstri 4.0 ile önemi artan dijital ikiz kavramının yeri, önemi, verimliliği ve gelecekteki potansiyelini anlamak, diğer çalışmalar için temel oluşturmak adına literatüre katkı sağlamaktır. Yöntem: Bu çalışmanın yöntemi literatür taramasıdır. Dijital ikiz ve işletmelerde kullanımı ile ilgili en güncel makaleler, bildiriler, gazete ve internet haberlerine dair örnek uygulamalar incelenerek, konu ile ilgili derinlemesine bir derleme makale çalışması yapılmıştır. Bulgular: Değerlendirme sonuçlarına göre, dijital ikiz teknolojisi işletmeler için bir çözüm yoludur. Dijital ikiz teknolojisinin katkısı ile siber fiziksel sistemler ve nesnelerin interneti başta olmak üzere Endüstri 4.0 teknolojileri daha etkin kullanılabilmektedir. Dijital ikiz, esnek, daha hızlı, kaliteli ve kişiselleştirilmiş ürünler elde edilmesine yardımcı olmaktadır. İşletmelerin tahmin etme, güvenlik, teşhis etme ve mevcut durumu sorgulamasında önemli avantaj sağlayan dijital ikizler, karar vermede isabet gücünü, ürün ve hizmetlerde kaliteyi, operasyonel faaliyetlerde hızı artırarak, toplamda işletme verimliliğini ve örgütsel performansı iyileştirip işletmelerin rekabet gücünü artırmaktadır. Özgünlük: Dijital ikiz kavramının yeni ortaya çıkmasına bağlı olarak literatürde önemli bir boşluk bulunmaktadır. Bu nedenle makale, dijital ikiz hakkında bilgi veren, üretimde faydaları ve uygulamaları üzerine odaklanan ilk yerli çalışmalar arasındadır.

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“…However, the proposed model has (a) a physical layer, (b) network layer, (c) database layer, (d) model layer, and (e) application layer. In 2021, Lugarsi et al [28] advanced the concept of DT using automatic model generation from the data. This increases the fidelity of the DT.…”

Section: Topic Clustermentioning

confidence: 99%

Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview

2022

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The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have recently been published. However, there is still a lack of a systematic literature review on different aspects of ML-based DT in the manufacturing industry from a bibliometric and evolutionary perspective. Therefore, the proposed study is mainly aimed at reviewing DT in the manufacturing industry to identify the contribution of ML, current methods, and future research directions. According to the findings, the contribution of ML to this domain is significant. Additionally, the results show that the latest ML technologies are being used in the DT domain; neural networks have evolved based on application-specific requirements. The total number of papers and citations per paper on ML-based DT is increasing. The relevance of ML in DT has increased over time. The current trend is to use ML-based DT for data analytics. Additionally, there are many unfilled gaps; certain gaps include industrial applications of DT, synchronisation with real-time data through sensors, heterogeneous data management, and benchmarking.

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Automated manufacturing system discovery and digital twin generation

Cited by 80 publications

References 43 publications

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

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

A New Productivity Technology for Business: Digital Twin

Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview

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