Digital Twin for Designing and Reconfiguring Human–Robot Collaborative Assembly Lines

Kousi, Niki; Gkournelos, Christos; Aivaliotis, Sotiris; Lotsaris, Konstantinos; Bavelos, Angelos Christos; Baris, Panagiotis; Michalos, George; Makris, Sotiris

doi:10.3390/app11104620

Cited by 75 publications

(24 citation statements)

References 29 publications

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“…Moreover, Universal Robots’ ecology is thriving thanks to the popularity of robots among researchers. There are many publications, mathematic models, simulations, and advanced control techniques based on these robots [ 42 , 43 , 44 ]. The advanced implementation can be developed based on the existing technology.…”

Section: Methodsmentioning

confidence: 99%

Digital Twin for a Collaborative Painting Robot

Chancharoen

Chaiprabha

Wuttisittikulkij

et al. 2022

Sensors

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A collaborative painting robot that can be used as an alternative to workers has been developed using a digital twin framework and its performance was demonstrated experimentally. The digital twin of the automatic painting robot simulates the entire process and estimates the paint result before the real execution. An operator can view the simulated process and result with an option to either confirm or cancel the task. If the task is accepted, the digital twin generates all the parameters, including the end effector trajectory of the robot, the material flow to the collaborative robot, and a spray mechanism. This ability means that the painting process can be practiced in a virtual environment to decrease set costs, waste, and time, all of which are highly demanded in single-item production. In this study, the screen was fixtureless and, thus, a camera was used to capture it in a physical environment, which was further analyzed to determine its pose. The digital twin then builds the screen in real-time in a virtual environment. The communication between the physical and digital twins is bidirectional in this scenario. An operator can design a painting pattern, such as a basic shape and/or letter, along with its size and paint location, in the resulting procedure. The digital twin then generates the simulation and expected painting result using the physical twin’s screen pose. The painting results show that the root mean square error (RMSE) of the painting is less than 1.5 mm and the standard deviation of RMSE is less than 0.85 mm. Additionally, the initial benefits of the technique include lower setup costs, waste, and time, as well as an easy-to-use operating procedure. More benefits are expected from the digital twin framework, such as the ability of the digital twin to (1) find a solution when a fault arises, (2) refine the control or optimize the operation, and (3) plan using historic data.

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Section: Methodsmentioning

confidence: 99%

Digital Twin for a Collaborative Painting Robot

Chancharoen

Chaiprabha

Wuttisittikulkij

et al. 2022

Sensors

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“…DT emerged as a concept for monitoring processes and is evolving towards being an instrument used for reconfiguration. They can be used in parallel with an artificial intelligence approach to reconfiguring human-robot collaborative assembly lines [14,15]; they model the real world and exchange data back and forth with the various assets of a manufacturing environment to be able to affect the system's decisions and behaviour [16]; and they can represent the base on which to build smart, fast and responsive manufacturing systems based on robotics assets [17,18].…”

Section: Related Workmentioning

confidence: 99%

A Framework for Manufacturing System Reconfiguration Based on Artificial Intelligence and Digital Twin

Chaplin

Sanderson

et al. 2022

Lecture Notes in Mechanical Engineering

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The application of digital twins and artificial intelligence to manufacturing has shown potential in improving system resilience, responsiveness, and productivity. Traditional digital twin approaches are generally applied to single, static systems to enhance a specific process. This paper proposes a framework that applies digital twins and artificial intelligence to manufacturing system reconfiguration, i.e., the layout, process parameters, and operation time of multiple assets, to enable system decision making based on varying demands from the customer or market. A digital twin environment has been developed to simulate the manufacturing process with multiple industrial robots performing various tasks. A data pipeline is built in the digital twin with an API (application programming interface) to enable the integration of artificial intelligence. Artificial intelligence methods are used to optimise the digital twin environment and improve system decision-making. Finally, a multi-agent program approach shows the communication and negotiation status between different agents to determine the optimal configuration for a manufacturing system to solve varying problems. Compared with previous research, this framework combines distributed intelligence, artificial intelligence for decision making, and production line optimisation that can be widely applied in modern reactive manufacturing applications.

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“…In [31], the authors propose the Digital Twin (DT) approach to address the complexity of achieving the full potential of Human-Robot Collaboration in assembly application. Kousi et al [32] proposed a DT based approach for designing and redesigning flexible assembly system. The advantage resulting from the use of the DT is the possibility to dynamically update or reconfigure the workplace layout in response to the occurrence of unexpected events.…”

Section: State Of Artmentioning

confidence: 99%

Collaborative Workplace Design: A Knowledge-Based Approach to Promote Human–Robot Collaboration and Multi-Objective Layout Optimization

et al. 2021

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The innovation-driven Industry 5.0 leads us to consider humanity in a prominent position as the center of the manufacturing field even more than Industry 4.0. This pushes us towards the hybridization of manufacturing plants promoting a full collaboration between humans and robots. However, there are currently very few workplaces where effective Human–Robot Collaboration takes place. Layout designing plays a key role in assuring safe and efficient Human–Robot Collaboration. The layout design, especially in the context of collaborative robotics, is a complex problem to face, since it is related to safety, ergonomics, and productivity aspects. In the current work, a Knowledge-Based Approach (KBA) is adopted to face the complexity of the layout design problem. The framework resulting from the KBA allows for developing a modeling paradigm that enables us to define a streamlined approach for the layout design. The proposed approach allows for placing resource within the workplace according to a defined optimization criterion, and also ensures compliance with various standards. This approach is applied to an industrial case study in order to prove its feasibility. A what-if analysis is performed by applying the proposed approach. Changing three control factors (i.e., minimum distance, robot speed, logistic space configuration) on three levels, in a Design of Experiments, 27 layout configurations of the same workplace are generated. Consequently, the inputs that most affect the layout design are identified by means of an Analysis of Variance (ANOVA). The results show that only one layout is eligible to be the best configuration, and only two out of three control factors are very significant for the designing of the HRC workplace layout. Hence, the proposed approach enables the designing of standard compliant and optimized HRC workplace layouts. Therefore, several alternatives of the layout for the same workplace can be easily generated and investigated in a systematic manner.

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Digital Twin for Designing and Reconfiguring Human–Robot Collaborative Assembly Lines

Cited by 75 publications

References 29 publications

Digital Twin for a Collaborative Painting Robot

Digital Twin for a Collaborative Painting Robot

A Framework for Manufacturing System Reconfiguration Based on Artificial Intelligence and Digital Twin

Collaborative Workplace Design: A Knowledge-Based Approach to Promote Human–Robot Collaboration and Multi-Objective Layout Optimization

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