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Smart manufacturing based on Digital Twin technologies
... In the robotics industry, the DT constructs a cyber-physical system (CPS) (Aheleroff et al. 2020) where information of the current and forecast future states of the robot can be displayed for decision-making and evaluation prior to task execution (Freedy et al. 2007). The work plan of the robot can also be determined in the DT and subsequently executed on the physical robot. ...
... The virtual model can be extracted from the designed construction model such as building information model (BIM) or scanned 3D point clouds of the as-built environment (Delbrügger et al. 2017;Marshall and Redovian 2019;Lu et al. 2020a). On the other hand, a communication mechanism is required to synchronize the data between the physical environment and the virtual model (Wang et al. 2019;Aheleroff et al. 2020). The communication needs to be bi-directional so that the virtual model can reflect the changes of the physical environment, and the user can determine the next steps in the virtual model and send the command to the physical environment. ...
This research focuses on developing a robot digital twin (DT) and the communication methods to connect it with the corresponding physical robot in collaborative human–robot construction work. Robots are being increasingly deployed on construction sites to assist human workers with physically demanding work tasks. Robot simulations in a process-level DT can be used to extend design models, such as building information modeling, to the construction phase for real-time monitoring of robot motion planning and control. Robots can be enabled to plan work tasks and execute them in the DT simulations. Once simulated tasks and trajectories are approved by human workers, commands can be sent to the physical robots to perform the tasks. However, a system to bridge a virtual DT and a physical robot and allow for such communication to occur is a capability that has not been readily available thus far, primarily due to the complexity involved in physical robot operations. This paper discusses the development of a system to bridge robot simulations and physical robots in construction and digital fabrication. The Gazebo robot simulator is used for DT, and the robot operating system is leveraged as the primary framework for bi-directional communication with the physical robots. The virtual robots in Gazebo receive planned trajectories from motion planners and then send the commands to the physical robots for execution. Two different robot control modes, i.e., joint angle control mode and Cartesian path control mode, are developed to accommodate various construction strategies. The system is implemented in a digital fabrication case study with a full-scale KUKA KR120 six-degrees-of-freedom robotic arm mounted on a track system. We evaluated the system by comparing the data transmission time, joint angles, and end-effector pose between the virtual and physical robot using several planned trajectories and calculated the average and maximum mean square errors. The results showed that the proposed real-time process-level robot DT system can plan the robot trajectory inside the virtual environment and execute it in the physical environment with high accuracy and real-time performance, offering the opportunity for further development and deployment of the collaborative human–robot work paradigm on real construction sites.
... The Digital Twin (DT) offers opportunities to virtually mimic the conditions of the physical (real) environment in allowing for a cyber-physical system (CPS) [9] where information of the current and forecasted future states of the robot can be displayed [5]. Figure 1 shows the physical robotic arm and its Digital Twin. Madni et al. [10] defined four levels of Digital Twin (Pre-Digital Twin, Digital Twin, Adaptive Digital Twin, and Intelligent Digital Twin) based on the level of intelligence. ...
... The virtual model can be extracted from the designed construction model such as BIM or scanned 3D point cloud of the as-built environment [12,13]. On the other hand, a communication mechanism is required to synchronize the data between the physical environment and the virtual model [9]. The communication is bi-directional so that the virtual model can reflect the changes of the physical environment, and the user can determine the next steps in the virtual model and send the command to the physical environment. ...
... The second category, the layer based approach (LBA), describes the design of a DT system generally consisting of three layers [14,21,53,78]. These three general layers are the physical layer, virtual layer and connection layer/information processing layer [79,80]. Figure 6 provides a conceptual overview of modelling the DT-HRC using LBA. ...
Industry 4.0, as an enabler of smart factories, focuses on flexible automation and customization of products by utilizing technologies such as the Internet of Things and cyber–physical systems. These technologies can also support the creation of virtual replicas which exhibit real-time characteristics of a physical system. These virtual replicas are commonly referred to as digital twins. With the increased adoption of digitized products, processes and services across manufacturing sectors, digital twins will play an important role throughout the entire product lifecycle. At the same time, collaborative robots have begun to make their way onto the shop floor to aid operators in completing tasks through human–robot collaboration. Therefore, the focus of this paper is to provide insights into approaches used to create digital twins of human–robot collaboration and the challenges in developing these digital twins. A review of different approaches for the creation of digital twins is presented, and the function and importance of digital twins in human–robot collaboration scenarios are described. Finally, the paper discusses the challenges of creating a digital twin, in particular the complexities of modelling the digital twin of human–robot collaboration and the exactness of the digital twin with respect to the physical system.
... Digital Twin provides a physical entity's digital replica that fulfils mass personalisation [56]. Digital Twin can make a digital replica of wanted characteristics, material, appearance, functionality, process, and system [57], extending to a personalised product's digital replica. Aheleroff et al. [58] proposed a Digital Twin reference architecture and explored the bi-directional relation between the physical artefact and the digital model [59] for meeting mass personalisation. ...
The Fourth Industrial Revolution (Industry 4.0) leads to mass personalisation as an emerging manufacturing
paradigm. Mass personalisation focuses on uniquely made products to individuals at scale. Global challenges
encourage mass personalisation manufacturing with efficiency competitive to mass production. Driven by
individualisation as a trend and enabled by increasing digitalisation, mass personalisation can go beyond today’s
mass customisation. This paper aims to introduce Mass Personalisation as a Service (MPaaS) to address unique
and complex requirements at scale by harnessing Industry 4.0 technologies, including Internet of Things, Additive
Manufacturing, Big Data, Cloud Manufacturing, Digital Twin, and Blockchain. A case study for the
implementation of MPaaS in personalised face masks is presented. The workforce with constant exposure to
contaminants requires personal protective equipment (PPE), such as facemasks, for longer hours resulting in
pressure-related ulcers. This prolonged use of PPE highlights the importance of personalisation to avoid ulcers
and other related health concerns. Most studies have used Additive Manufacturing for individualisation and
cloud capabilities for large-scale manufacturing. This study develops a framework and mathematical model to
demonstrate the capability of the proposed solution to address one of the most critical challenges by making
personalised face masks as an essential PPE in the critical industrial environment.
... Therefore, the value lifecycle axis plays a considerable role in this reference model due to the similarity between Agile and Twinning concepts as both are suitable for individualization. Fig. 8 shows that Digital Twin involved in consequence of requirement gathering, functionality & appearance requirement analysis, Digital Twin-enabled monitoring, controlling, test, and Additive Manufacturing through an iterative, incremental process [55]. This Agile methodology can provision mass individualization under Industry 4.0 umbrella. ...
Recent findings have shown that Digital Twin served multiple constituencies. However, the dilemma between the scope and scale needs a sophisticated reference architecture, a right set of technologies, and a suitable business model. Most studies in the Digital Twin field have only focused on manufacturing and proposed explicit frameworks and architecture, which faced challenges to support different integration levels through an agile process. Besides, no known empirical research has focused on exploring relationships between Digital Twin and mass individualization. Therefore, the principal objective of this study was to identify suitable Industry 4.0 technologies and a holistic reference architecture model to accomplish the most challenging Digital Twin enabled applications. In this study, a Digital Twin reference architecture was developed and applied in an industrial case. Also, Digital Twin as a Service (DTaaS) paradigm utilized for the digital transformation of unique wetlands with considerable advantages, including smart scheduled maintenance, real-time monitoring, remote controlling, and predicting functionalities. The findings indicate that there is a significant relationship between Digital Twin capabilities as a service and mass individualization.
Digital Twin is a virtual representation of objects, processes, and systems that exist in real-time. While Digital Twin can represent digital objects, they are often used to connect the physical and digital worlds. This technology plays a vital role in fulfilling various requirements of Industry 4.0. It gives a digital image of a factory's operations, a communications network's activities, or the movement of items through a logistics system. This paper studies Digital Twin and its need in Industry 4.0. Then the process and supportive features of Digital Twin for Industry 4.0 are diagrammatically discussed, and finally, the major applications of Digital Twin for Industry 4.0 are identified. Digital Twin sophistication depends on the process or product represented and the data available. Manufacturers can learn how assets will behave in real-time, in the physical world, by putting sensors on particular assets, gathering data, creating digital duplicates, and employing machine intelligence. They can confidently make wise judgments, which helps improve company performance. Digital Twin assesses material usage to save costs, discover inefficiencies, replicate tool tracking systems, and do other things. Manufacturers construct a digital clone for specific equipment and tools, exclusive products or systems, entire procedures, or anything else they want to improve on the factory floor. Sensors and other equipment that collect real-time data on the state of the process or product collect this information, which on the other hand, must be handled and processed appropriately. It is made feasible by IoT sensors, which collect data from the physical environment and transmit it to be virtually recreated. This information comprises design and engineering details that explain the asset's shape, materials, components, and behaviour or performance.
The increasing digitalization of production processes and current technological developments make it possible to use sophisticated digital product models or virtual images of industrial and technical processes, so called digital twins. In this paper a digital twin is created using different development environments and systems, such as Simulink®, B&R® control, CAD design environment and OPC-UA communication. The creation process used is verified and simulations are performed with the created digital twin to test and validate its behavior and efficiency compared with the physical model. Then the two systems, which are the digital twin and the elevator model, are linked together so that the communication between the two can be verified. The ability to communicate with the real world is one of the main characteristics of an optimal digital twin.
Future factories will feature strong integration of physical machines and cyber-enabled software, working seamlessly to improve manufacturing production efficiency. In these digitally enabled and network connected factories, each physical machine on the shop floor can have its ‘virtual twin’ available in cyberspace. This ‘virtual twin’ is populated with data streaming in from the physical machines to represent a near real-time as-is state of the machine in cyberspace. This results in the virtualization of a machine resource to external factory manufacturing systems. This paper describes how streaming data can be stored in a scalable and flexible document schema based database such as MongoDB, a data store that makes up the virtual twin system. We present an architecture, which allows third-party integration of software apps to interface with the virtual manufacturing machines. We evaluate our database schema against query statements and provide examples of how third-party apps can interface with manufacturing machines using the VMM middleware. Finally, we discuss an operating system architecture for VMMs across the manufacturing cyberspace, which necessitates command and control of various virtualized manufacturing machines, opening new possibilities in cyber-physical systems in manufacturing.