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In this article, a digital twin approach is proposed for modeling a pharmaceutical drying process using machine learning techniques, driven by data from different sensors captured in-line. The current difficulty with the drying process is mainly due to the manual operator control for choosing the end-point for terminating the drying step. This resu...
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... Due to the importance of modeling in the practice of digital twin workshops, several researchers have conducted some modeling efforts. They use a pre-defined model base framework, and then continuously improve it through the machine learning paradigm to eventually become a mature digital twin model [32][33][34]. This approach requires relatively less manpower, but the final production model data structure style uncertainty is larger, so it is difficult to generate a unified data Content courtesy of Springer Nature, terms of use apply. ...
Digital twins have attracted more and more attention in the past few years. To put digital twins into practice, a large number of modeling approaches have been proposed, vast amounts of data have been collected, and their accuracy has been improving. However, current research has paid insufficient attention to the multi-scale features of the shop floor, which hinders the effective application of the digital twin shop floor. To address the problem of how to achieve effective multi-level and multi-dimensional fusion of digital twin models with production process data, this paper first proposes a structured data modeling framework for sorting out all the production process data collected in real-time; and then proposes a multi-level fusion framework for supporting the fusion of real-time data and twin models from the unit level to the system level. The method judges the parsed received data streams through the full-factor semanticization framework, and at the same time fuses the parsed data streams with the constructed full-factor twin model from multiple dimensions and layers, forming a twin model fusion method with real-time data streams as the blood and twin model as the skeleton. Finally, the micro-assembly-based production shop environment is selected as a case study to verify the correctness and feasibility of the proposed data grooming framework, data, and model fusion method.
... Due to the importance of modeling in the practice of digital twin workshops, several researchers have conducted some modeling efforts. They use a pre-defined model base framework, and then continuously improve it through the machine learning paradigm to eventually become a mature digital twin model [32][33][34]. This approach requires relatively less manpower, but the final production model data structure style uncertainty is larger, so it is difficult to generate a unified data grooming framework. ...
Digital twins have attracted more and more attention in the past few years. To put digital twins into practice, a large number of modeling approaches have been proposed, vast amounts of data have been collected, and their accuracy has been improving. However, current research has paid insufficient attention to the multi-scale features of the shop floor, which hinders the effective application of the digital twin shop floor. To address the problem of how to achieve effective multi-level and multi-dimensional fusion of digital twin models with production process data, this paper first proposes a structured data modeling framework for sorting out all the production process data collected in real-time; and then proposes a multi-level fusion framework for supporting the fusion of real-time data and twin models from the unit level to the system level. The method judges the parsed received data streams through the full-factor semanticization framework, and at the same time fuses the parsed data streams with the constructed full-factor twin model from multiple dimensions and layers, forming a twin model fusion method with real-time data streams as the blood and twin model as the skeleton. Finally, the micro-assembly-based production shop environment is selected as a case study to verify the correctness and feasibility of the proposed data grooming framework, data, and model fusion method.
... Nonetheless, the focus of this paper is on the evaluation of the KPIs of the ice cream company using a DT model of the system, while the control part is not embodied in the model. Another example of DT implementation, even if in a different process industry (i.e. the pharmaceutical one), has been reported by Barriga et al. (2022), who have proposed a DT approach for modelling a drying process. The proposed approach makes use of machine learning techniques and retrieves data from different sensors captured in-line from the plant. ...
