Outlook on human-centric manufacturing towards Industry 5.0

Lu, Yuqian; Zheng, Hao; Chand, Saahil; Xia, Wanqing; Liu, Zengkun; Xu, Xun; Wang, Lihui; Qin, Zhaojun; Bao, Jinsong

doi:10.1016/j.jmsy.2022.02.001

Cited by 336 publications

(128 citation statements)

References 132 publications

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“…To date, reference models for human-centric manufacturing approach this goal from a more production-technology related angle (cf. Lu et al 2022). As the topic per se is transdisciplinary, shared or at least known agreed and mutually accepted models across the disciplines of engineering, human factors, humancomputer engineering are needed.…”

Section: Discussionmentioning

confidence: 99%

Towards Designing Adaptive and Personalized Work Systems in Manufacturing

Schlund¹,

Kostolani²

2022

Digitization of the Work Environment for Sustainable Production

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Adaptation of work to humans instead of humans constantly adapting to work systems and work environments has been a goal of ergonomics since the 19th century. To date, an actual adaptation of work to the specific features, conditions and requirements of individual users has so far only taken place in rudimentary form. The possibilities have now opened up by the advances of Cyber Physical Production Systems (CPPS). This enables a renewed attempt to implement the goal of adapting work systems to workers. Integration of advanced sensory skills into work systems within manufacturing allows better recognition of the work environment and the actual state of the system. Together with actuator capabilities, it enables adaptive work systems to adjust their structure and behaviour to the anthropometric and the cognitive features and requirements of the systems’ users. However, a framework that integrates the concepts of adaptivity and personalization into the work system model and work system design is still missing. This paper explores the roots of adaptive and personalized human-machine systems and shows a framework for integrating these concepts into work systems and manufacturing. Based on this, exemplary works and implementations of individual systems are presented and placed in the overall context. The focus is set on implementations within the TU Wien Pilot Factory that cover adaptability concepts on workplace, process, and system level.

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Section: Discussionmentioning

confidence: 99%

Towards Designing Adaptive and Personalized Work Systems in Manufacturing

Schlund¹,

Kostolani²

2022

Digitization of the Work Environment for Sustainable Production

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“…Safety-related information can also be visualized to the operator, increasing his/her safety feeling in the cell (Makris et al 2016). Lu et al (2022) described the human-machine relationship as a 5C journey: coexistence, cooperation, collaboration, compassion, and coevolution. To arrive at a scenario where empathic machines provide situational assistance to humans and both learn from each other, a human-centric HRC needs to be designed.…”

Section: Human-robot Interaction and Safety Issuesmentioning

confidence: 99%

A human-oriented design process for collaborative robotics

Papetti

Ciccarelli

Scoccia

et al. 2022

International Journal of Computer Integrated Manufacturing

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The potential of collaborative robotics often does not materialize in an efficient design of the human-robot collaboration. Technology-oriented approaches are no longer enough in the Industry 4.0 era. This work proposes a set of methods to support manufacturing engineers in the humanoriented design process of integrated production systems to obtain satisfactory performance in the mass customization paradigm, without impacting the safety and health of workers. It founds the design criteria definition on five main pillars (safety, ergonomics, effectiveness, flexibility, and costs), favors the consideration of different design alternatives, and leads their selection. The dynamic impact of the design choices on the various elements of the system prevails over the static design constraints. The method has been experimented in collaboration with the major kitchen manufacturer in Italy, which introduced a collaborative robotics cell in the drawers' assembly line. It resulted in a more balanced production line (10% more), a verified risk minimization (RULA score reduced from 5 to 3 and OCRA score from 13.30 to 5.70), and a greater allocation of operators to high added value activities.

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“…As we discussed before, the transition to the so-called Industry 5.0 era requires specialists and domain experts to be able to learn new concepts quickly and improve their qualifications intuitively [6]. A general term used often for such workplace training activities is Work-based or Workintegrated learning [7].…”

Section: Related Workmentioning

confidence: 99%

Hey Max, can you help me? An Intuitive Virtual Assistant for Industrial Robots

Li¹,

Chrysostomou²,

Pinto³

et al. 2022

Preprint

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<p>Assisting employees in acquiring the knowledge and skills necessary to grasp and use new services and technologies on the shop floor is critical for manufacturers to adapt to Industry 4.0 successfully. In this paper, we employ a Learning, Training, Assistance (LTA) approach and propose a framework for a Language-enabled Virtual Assistant (VA) to facilitate this adaptation. In our system, the human-robot interaction is achieved through spoken natural language and a dashboard implemented as a web-based application. This type of interaction enables operators of all levels to control a collaborative robot intuitively in several industrial scenarios and use it as a complementary tool for developing their competencies. Our proposed framework has been extensively tested with 29 users who completed various tasks while interacting with the proposed VA and industrial robots. Through three different scenarios, we evaluated the usability of the system for LTA based on an established System Usability Scale and the cognitive effort required by the users based on the standardised NASA TLX questionnaire. The qualitative and quantitative results of the study show that users of all levels found the VA user friendly with low requirements for physical and mental effort during the interaction. Additionally, the study demonstrates that the VA enables operators to streamline the learning and training phases of new tasks and improve their user experience during the assistance phase for daily tasks. The source code of the proposed VA and the supplementary material of the user study are accessible at https://bit.ly/VA MAX to support the reproducibility of the proposed framework.</p>

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Outlook on human-centric manufacturing towards Industry 5.0

Cited by 336 publications

References 132 publications

Towards Designing Adaptive and Personalized Work Systems in Manufacturing

Towards Designing Adaptive and Personalized Work Systems in Manufacturing

A human-oriented design process for collaborative robotics

Hey Max, can you help me? An Intuitive Virtual Assistant for Industrial Robots

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