Skip to Main content Skip to Navigation
Conference papers

Human-machine cooperation to design Intelligent Manufacturing Systems

Abstract : Since the start of industrialization, machine capabilities have increased in such a way that the control of processes by humans is becoming increasingly complex. This is especially the case in Intelligent Manufacturing Systems for which processes tend to be so autonomous that humans are more and more unaware of processes running, particularly when humans may need to intervene to update the manufacturing plan or to modify the process configuration if machines or intelligent entities need assistance. The present paper proposes solutions based on the use of Human(s)-Machine(s) Cooperation (HMC) principles to support humans in the process control. The aim of these principles is to adopt a human-centered approach for the design and evaluation of assistance systems and processes, as well as their interaction with humans. Two main complementary features of HMC, the know-how and the know-how-to-cooperate, are detailed. They provide a very useful approach to design task allocation, support for mutual understanding and communication between one human operator and one Artificial Self Organizing system. An assistance system resulting from this approach was evaluated and first results highlighted the improvement of global performance and acceptability.
Document type :
Conference papers
Complete list of metadata

https://hal-uphf.archives-ouvertes.fr/hal-03416131
Contributor : Kathleen Torck Connect in order to contact the contributor
Submitted on : Friday, November 5, 2021 - 9:52:05 AM
Last modification on : Saturday, November 6, 2021 - 4:21:31 AM

Identifiers

Collections

Citation

Marie-Pierre Pacaux-Lemoine, Damien Trentesaux, Gabriel Zambrano Rey. Human-machine cooperation to design Intelligent Manufacturing Systems. IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, Oct 2016, Florence, Italy. pp.5904-5909, ⟨10.1109/IECON.2016.7793180⟩. ⟨hal-03416131⟩

Share

Metrics

Record views

6