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Figure 2. The interfacing levels of the interoperability model (adapted from [6])

Figure 2 The interfacing levels of the interoperability model (adapted from [6])

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to their deployment. The paper will also analyse how digital twins can meet the challenge of providing an interoperable system across multiple sites if needed. As a key enabling technology, edge processing will be reviewed and the deployment of machine learning algorithms in smart manufacturing will also be assessed.
to their deployment. The paper will also analyse how digital twins can meet the challenge of providing an interoperable system across multiple sites if needed. As a key enabling technology, edge processing will be reviewed and the deployment of machine learning algorithms in smart manufacturing will also be assessed.
Figure 3. Schematic of semantic interoperability between different IoT platforms.
Figure 3. Schematic of semantic interoperability between different IoT platforms.
Figure 4. Interoperability of different entities in smart manufacturing.
Figure 4. Interoperability of different entities in smart manufacturing.
Figure 5. Progression toward a system of systems for digital twins.
Figure 5. Progression toward a system of systems for digital twins.
Artificial intelligence’s branch of machine learning relies on the notion that computers can discover patterns in data and make judgments without much human input. Many studies have been carried out on how to make machines learn by themselves. A recent study by Cadavid et al. [9] presents a wide variety of ML applications in Industry 4.0. However, these ML applications generally focus on aspects of the production process itself, such as planning and scheduling, smart maintenance, quality control, etc. [25]. An overview of the multitude of ML types is shown below in Figure 6.
Artificial intelligence’s branch of machine learning relies on the notion that computers can discover patterns in data and make judgments without much human input. Many studies have been carried out on how to make machines learn by themselves. A recent study by Cadavid et al. [9] presents a wide variety of ML applications in Industry 4.0. However, these ML applications generally focus on aspects of the production process itself, such as planning and scheduling, smart maintenance, quality control, etc. [25]. An overview of the multitude of ML types is shown below in Figure 6.
Digital twins are unique because no two businesses will have the same digital twin architecture as their assets; processes and facilities will differ. The beauty of a digital twin is that it is a mirror of the physical workspace. As the business grows and changes, so too erstanding systems, their architecture, behaviours, and ems depends on conceptual models. Such models must be codified and standardised in a way that is reusable for various use cases encountered lions and billions of interconnections are implemen will the digital counterpart. Und how they interact with other sys in the field in a world where mi daily in dynamic ways. By mode ling information, ed professionals can develop standardised models depicting the fundamental components of systems and intelligent machines, result- ing in better modularity and reuse. A digital twin holistic understanding of the rea world for the bes a structure, utility, community, nation, or another connection between two subsystems, but all information transmitted across connec systems must assist the digital twin’s overall goal: erability mechanism, in which each system contributes to the collective intelligence of whole and where one system may easily consume and respond to information from anot system [31]. Below in Figure 7 is a diagram that captures the concept of an interopera system as outlined. system’s primary goal is to speed decision-making. The “world” can environment. Not simply the isola he be ed ed he development of a common interop- he her ble Figure 7. Conceptual digital twin framework towards a system of systems.
Digital twins are unique because no two businesses will have the same digital twin architecture as their assets; processes and facilities will differ. The beauty of a digital twin is that it is a mirror of the physical workspace. As the business grows and changes, so too erstanding systems, their architecture, behaviours, and ems depends on conceptual models. Such models must be codified and standardised in a way that is reusable for various use cases encountered lions and billions of interconnections are implemen will the digital counterpart. Und how they interact with other sys in the field in a world where mi daily in dynamic ways. By mode ling information, ed professionals can develop standardised models depicting the fundamental components of systems and intelligent machines, result- ing in better modularity and reuse. A digital twin holistic understanding of the rea world for the bes a structure, utility, community, nation, or another connection between two subsystems, but all information transmitted across connec systems must assist the digital twin’s overall goal: erability mechanism, in which each system contributes to the collective intelligence of whole and where one system may easily consume and respond to information from anot system [31]. Below in Figure 7 is a diagram that captures the concept of an interopera system as outlined. system’s primary goal is to speed decision-making. The “world” can environment. Not simply the isola he be ed ed he development of a common interop- he her ble Figure 7. Conceptual digital twin framework towards a system of systems.
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