Designing human–system cooperation in industry 4.0 with cognitive work analysis: a first evaluation

Pacaux‐Lemoine, Marie‐Pierre; Berdal, Quentin; Guerin, Clément; Rauffet, Philippe; Chauvin, Christine; Trentesaux, Damien

doi:10.1007/s10111-021-00667-y

Cited by 32 publications

(19 citation statements)

References 38 publications

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“…In some cases, the operator could get help from an assistant with cognitive skills to improve the operator's understanding of technology equipment. Hence, in [10], a cognitive work analysis method is applied for the design of an assistance system to support humans in the control of intelligent manufacturing systems. In [11], an intelligent decision making method is developed that allows human-robot task allocation using the robot operating system (ROS) framework.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

2021

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In human–robot collaborative assembly tasks, it is necessary to properly balance skills to maximize productivity. Human operators can contribute with their abilities in dexterous manipulation, reasoning and problem solving, but a bounded workload (cognitive, physical, and timing) should be assigned for the task. Collaborative robots can provide accurate, quick and precise physical work skills, but they have constrained cognitive interaction capacity and low dexterous ability. In this work, an experimental setup is introduced in the form of a laboratory case study in which the task performance of the human–robot team and the mental workload of the humans are analyzed for an assembly task. We demonstrate that an operator working on a main high-demanding cognitive task can also comply with a secondary task (assembly) mainly developed for a robot asking for some cognitive and dexterous human capacities producing a very low impact on the primary task. In this form, skills are well balanced, and the operator is satisfied with the working conditions.

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Section: Introductionmentioning

confidence: 99%

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

2021

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“…For these reasons, the humanmachine symbiosis must be based on rules for adapting functions and interaction conditions (Romero, Wuest, Stahre and Gorecky, 2017 ;Steijn, Oosterhout, Willemsen & Jansen, 2020). The CWA method has already been used in research on humanmachine cooperation for Industry 4.0 (Guerin et al, 2019;Pacaux-Lemoine et al, 2021) but further studies are needed. These future works will improve our understanding of operator 4.0 or analytical operator, in cognitive interaction with technological systems (Romero et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Using Cognitive Work Analysis to Develop Predictive Maintenance Tool for Vessels

Simon¹,

Guerin²,

Rauffet³

et al. 2021

Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)

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Implementation of multisource sensors combined with data analysis systems (e.g. machine learning) might provide new solutions for predictive maintenance to improve sociotechnical system reliability. The Seanatic project aims to develop a decision support tool to increase maintenance processes in the maritime field, considering limits and benefits of human factor expertise. Under this perspective, this paper describes the Cognitive Work Analysis (CWA) approach for investigating new key functions that emerge in future maintenance sociotechnical systems. After phase one of the CWA was completed (WDA -Work Domain Analysis), the functions identified were used in the subsequent phases (ConTA -Control Task Analysis and SOCA -Social Organization and Cooperation Analysis) to highlight different implications for human cognitive activities. Realtime and prediction of machine breakdown of a vessel could be significantly reduced by assisting the chief engineer for supervision and planning activities. Based on a CWA approach, ecological design interfaces could support those activities.

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“…The tasks are as follows: (1) to plan and manage production; (2) to supervise and control mobile robots. This article is a follow-up to a previous study presented in [7]. It presents a complementary experiment and provides results about the human performance and workload with and without assistance systems when the human was controlling a complex IMS alone.…”

Section: Introductionmentioning

confidence: 94%

“…One of the main risks is the dependency of humans upon machines' ability and capacity, and therefore humans will be unable to complete tasks without machine support when necessary. In the manufacturing 4.0 domain, a recent study had pointed out the complexity of carrying out the task of controlling and supervising an IMS by a single human operator [7]. It was reported that the analysis of the spatial representation of the IMS was difficult for humans because they had to permanently split their attention between several screens.…”

Section: Review Of the Literature And Presentation Of The Research Objectivementioning

confidence: 99%

From Human-Human to Human-Machine Cooperation in Manufacturing 4.0

et al. 2021

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Humans are currently experiencing the fourth industrial revolution called Industry 4.0. This revolution came about with the arrival of new technologies that promise to change the way humans work and interact with each other and with machines. It aims to improve the cooperation between humans and machines for mutual enrichment. This would be done by leveraging human knowledge and experience, and by reactively balancing some complex or complicated tasks with intelligent systems. To achieve this objective, methodological approaches based on experimental studies should be followed to ensure a proper evaluation of human-machine system design choices. This paper proposes an experimental study based on a platform that uses an intelligent manufacturing system made up of mobile robots, autonomous shuttles using the principle of intelligent products, and manufacturing robots in the context of Manufacturing 4.0. Two experiments were conducted to evaluate the impact of teamwork human-machine cooperation, performance, and workload of the human operator. The results showed a lower level of participants’ assessment of time demand and physical demand in teamwork conditions. It was also found that the team working improves the subjective human operator Know-how-to-cooperate when controlling the autonomous shuttles. Moreover, the results showed that in addition to the work organization, other personal parameters, such as the frequency of playing video games could affect the performance and state of the human operator. They raised the importance of further analysis to determine cooperative patterns in a group of humans that can be adapted to improve human-machine cooperation.

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Designing human–system cooperation in industry 4.0 with cognitive work analysis: a first evaluation

Cited by 32 publications

References 38 publications

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

Using Cognitive Work Analysis to Develop Predictive Maintenance Tool for Vessels

From Human-Human to Human-Machine Cooperation in Manufacturing 4.0

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