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A DT-Based System for Predicting Process Behavior

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Advances in System-Integrated Intelligence (SYSINT 2022)

Abstract

Manufacturers can use a digital twin to obtain a better understanding of the performance and operational circumstances of a manufacturing asset through near real-time data collected from the asset and make proactive decisions about its optimal operation. Due to its great machining efficiency and good finish quality, grinding is one of the most used precision machining techniques. The vertical double side grinding machine does the necessary work while connected to the PLC program, and we established a methodology for process behavior prediction utilizing a digital twin approach in this paper. The proposed method creates a model of the grinding machine and calculates the grinding force and motor current value using data from sensors, physical models, and system operation. Results from simulations demonstrate how models and communication should be connected. The digital model was established to exactly match the operation of the physical system. Comparison between predicted result obtained from the proposed digital twin model and experiment, revealed a good agreement between proposed model and practice, indicating therefore that the model may be suitable for industrial applications further.

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Correspondence to Bowen Qi .

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Qi, B., Park, H. (2023). A DT-Based System for Predicting Process Behavior. In: Valle, M., et al. Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-16281-7_24

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  • DOI: https://doi.org/10.1007/978-3-031-16281-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16280-0

  • Online ISBN: 978-3-031-16281-7

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