Developing Process Execution Support for High-Tech Manufacturing Processes

Vanderfeesten, Irene; Erasmus, Jonnro; Traganos, Konstantinos; Bouklis, Panagiotis; Garbi, Anastasia; Boultadakis, George; Dijkman, Remco M.; Grefen, Paul

doi:10.1007/978-3-030-17666-2_6

Cited by 8 publications

(7 citation statements)

References 22 publications

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“…The components and their short explanations are presented in Table 2. On top of the M2O2P component, MPMS [68] was used to handle the process orchestration and the trajectory generator (TGT) was utilized to communicate with the collaborative robot. Such components fulfil the requirements for the dependent applications of M2O2P.…”

Section: Components and Architecturementioning

confidence: 99%

Multimodal Interface for Human–Robot Collaboration

et al. 2022

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Human–robot collaboration (HRC) is one of the key aspects of Industry 4.0 (I4.0) and requires intuitive modalities for humans to communicate seamlessly with robots, such as speech, touch, or bodily gestures. However, utilizing these modalities is usually not enough to ensure a good user experience and a consideration of the human factors. Therefore, this paper presents a software component, Multi-Modal Offline and Online Programming (M2O2P), which considers such characteristics and establishes a communication channel with a robot with predefined yet configurable hand gestures. The solution was evaluated within a smart factory use case in the Smart Human Oriented Platform for Connected Factories (SHOP4CF) EU project. The evaluation focused on the effects of the gesture personalization on the perceived workload of the users using NASA-TLX and the usability of the component. The results of the study showed that the personalization of the gestures reduced the physical and mental workload and was preferred by the participants, while overall the workload of the tasks did not significantly differ. Furthermore, the high system usability scale (SUS) score of the application, with a mean of 79.25, indicates the overall usability of the component. Additionally, the gesture recognition accuracy of M2O2P was measured as 99.05%, which is similar to the results of state-of-the-art applications.

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Section: Components and Architecturementioning

confidence: 99%

Multimodal Interface for Human–Robot Collaboration

et al. 2022

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“…by Friedow et al [50] and Suri et al [49]). While most of these approaches propose the integration of IoT-related aspects into business processes independent of a specific domain, various works address for example the industrial IoT/high-tech manufacturing domains (Industry 4.0) specifically and propose (business) process modelling, implementation and integration concepts with smart factories, production lines, their machines, robots and supply chains [51][52][53][54]. All these approaches propose new formalisms and extensions to integrate IoT-related tasks, events, activities and devices as resources into business processes.…”

Section: Iot Process Modelling and Execution On "Classical" Guismentioning

confidence: 99%

HoloFlows: modelling of processes for the Internet of Things in mixed reality

et al. 2021

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Our everyday lives are increasingly pervaded by digital assistants and smart devices forming the Internet of Things (IoT). While user interfaces to directly monitor and control individual IoT devices are becoming more sophisticated and end-user friendly, applications to connect standalone IoT devices and create more complex IoT processes for automating and assisting users with repetitive tasks still require a high level of technical expertise and programming knowledge. Related approaches for process modelling in IoT mostly suggest extensions to complex modelling languages, require high levels of abstraction and technical knowledge, and rely on unintuitive tools. We present a novel approach for end-user oriented-no-code-IoT process modelling using Mixed Reality (MR) technology: HoloFlows. Users are able to explore the IoT environment and model processes among sensors and actuators as first-class citizens by simply “drawing” virtual wires among physical IoT devices. MR technology hereby facilitates the understanding of the physical contexts and relations among the IoT devices and provides a new and more intuitive way of modelling IoT processes. The results of a user study comparing HoloFlows with classical modelling approaches show an increased user experience and decrease in required modelling knowledge and technical expertise to create IoT processes.

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“…Detailed process models were developed by the researchers and accepted by the appropriate factory managers to be used for demonstrations of the HORSE System. The authors documented the evolution from initial observations to executable manufacturing process models in [76].…”

Section: Practical Demonstrationmentioning

confidence: 99%

The HORSE Project: The Application of Business Process Management for Flexibility in Smart Manufacturing

et al. 2020

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Several high-tech manufacturing technologies are emerging to meet the demand for mass customized products. These technologies include configurable robots, augmented reality and the Internet-of-Things. Manufacturing enterprises can leverage these new technologies to pursue increased flexibility, i.e., the ability to perform a larger variety of activities within a shorter time. However, the flexibility offered by these new technologies is not fully exploited, because current operations management techniques are not dynamic enough to support high variability and frequent change. The HORSE Project investigated several of the new technologies to find novel ways to improve flexibility, as part of the Horizon 2020 research and innovation program. The purpose of the project was to develop a system, integrating these new technologies, to support efficient and flexible manufacturing. This article presents the core result of the project: a reference architecture for a manufacturing operations management system. It is based on the application and extension of business process management (BPM) to manage dynamic manufacturing processes. It is argued that BPM can complement current operations management techniques by acting as an orchestrator in manufacturing processes augmented by smart technologies. Building on well-known information systems’ architecting frameworks, design science research is performed to determine how BPM can be applied and adapted in smart manufacturing operations. The resulting reference architecture is realized in a concrete HORSE system and deployed and evaluated in ten practical cases, of which one is discussed in detail. It is shown that the developed system can flexibly orchestrate the manufacturing process through vertical control of all agents, and dynamic allocation of agents in the manufacturing process. Based on that, we conclude that BPM can be applied to overcome some of the obstacles toward increased flexibility and smart manufacturing.

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Developing Process Execution Support for High-Tech Manufacturing Processes

Cited by 8 publications

References 22 publications

Multimodal Interface for Human–Robot Collaboration

Multimodal Interface for Human–Robot Collaboration

HoloFlows: modelling of processes for the Internet of Things in mixed reality

The HORSE Project: The Application of Business Process Management for Flexibility in Smart Manufacturing

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