Strengthening Digital Twin Applications based on Machine Learning for Complex Equipment

Ren, Zijie

doi:10.23919/date51398.2021.9474133

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Simulation of physical process on ad hoc or continuous basis process simulation [63], automated simulation model generation, [64] Digital model richness Robustness, resilience, self-adaption, fidelity of virtual model Robustness, resilience, self-adaption, fidelity [44], DT fidelity [53], fidelity [61], DT behaviour model [37], high-fidelity of DTs [64] Human interaction Bridging human and machine Human-machine collaboration [5], bridges a human user and robot [25] Product life-cycle Product design, manufacturing and service Service stage: service, data analytics [38], Full product life-cycle management [37,63,65], Manufacturing stage: fault prediction [3], predicting energy efficiency [37], predictive maintenance, feature extraction [30] Figure 16 shows contribution of ML-based DT in manufacturing PLM. ML-based DT is marginally used in full product life-cycle management [37,63,65]. Conversely, in 92.68% of cases, ML-based DT is used for the product manufacturing stage.…”

Section: Simulation Capabilitiesmentioning

confidence: 99%

“…Additionally, big data analytics [67] and information weighting [40] appeared as a dominant future research directions in the period 2020-2022. In 2022 and onwards, the incorporation of time-series [65] and categorical data [36], encapsulation of works in progress [68], data heterogeneity [43], real-time data [63], and data quality improvement will be dominant in ML-based DT.…”

Section: Data-based Taskmentioning

confidence: 99%

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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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