Process design and supervision: A next generation simulation approach to digitalised manufacturing

Alexopoulos, Theocharis 2022. Process design and supervision: A next generation simulation approach to digitalised manufacturing. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

Modern processes will increasingly have a digital counterpart which is an interactive representation of the physical system integrated into a digital environment. At the heart of this digital counterpart are simulators that use raw data and calculation models to automate supervision tasks and increase process autonomy. As such, simulators have become a critical part of process digitalisation. But, despite the exponential increase of digitalisation related research simulators have not evolved to fully utilise the latest practices for data value extraction. This research work examines the current role of simulation within digitalised systems, identifies state-of-the-art simulator structural components and proposes a design architecture for next generation simulators. The proposed architecture provides a structured way to develop next generation simulation systems. At the same time, it embeds the latest data science related technologies into the simulator and enables the integration of the simulator with modern edge or cloud systems. To achieve that, the simulator is broken down into five elements and the function of each element is specified based on system performance, digital environment compatibility and development ease. To demonstrate the effectiveness of the architecture, the author developed a vertical machining centre simulator that uses a mesh-based method to represent the process and the latest automated machine learning techniques to generate knowledge from the information extracted by the monitoring data. To verify the capabilities of the simulator a series of experiments were performed on a vertical machining system with a focus on spindle load measurement. The results show that the developed simulator estimates spindle load accurately despite input data noise and within the time restrictions occurring in real-time applications. All generated knowledge is stored and accessible for future simulator runs and finally, the system demonstrates its ability to extract value from all available data while reducing the raw data storage needs.

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Engineering
Uncontrolled Keywords:	1). Digital Twin 2). CNC Simulation 3). Digital Manufacturing 4). CNC Process Modelling 5). Manufacturing Supervision 6). Simulator Architecture
Date of First Compliant Deposit:	1 June 2023
Last Modified:	01 Jun 2023 15:35
URI:	https://orca.cardiff.ac.uk/id/eprint/160112

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