Towards modern inclusive factories: A methodology for the development of smart adaptive human-machine interfaces

Abstract

Modern manufacturing systems typically require high degrees of flexibility, in terms of ability to customize the production lines to the constantly changing market requests. For this purpose, manufacturing systems are required to be able to cope with changes in the types of products, and in the size of the production batches. As a consequence, the human-machine interfaces (HMIs) are typically very complex, and include a wide range of possible operational modes and commands. This generally implies an unsustainable cognitive workload for the human operators, in addition to a non-negligible training effort. To overcome this issue, in this paper we present a methodology for the design of adaptive human-centred HMIs for industrial machines and robots. The proposed approach relies on three pillars: measurement of user's capabilities, adaptation of the information presented in the HMI, and training of the user. The results expected from the application of the proposed methodology are investigated in terms of increased customization and productivity of manufacturing processes, and wider acceptance of automation technologies. The proposed approach has been devised in the framework of the European project INCLUSIVE.

Evolution of Cognitive Demand in the Human–machine Interaction Integrated With Industry 4.0 Technologies

“…Furthermore, another aspect to consider is that the virtual and the augmented reality could be useful tools in training, as they allow the operator to act in dynamic and unforeseen situation safely also by means of on-line step-by-step procedures [11].…”

Methodological Approach for the Evaluation of an Adaptive and Assistive Human-Machine System

“…The proposed methodological approach will be applied on the adaptive human-machine systems that will be developed considering real industrial use cases within the EU H2020 INCLUSIVE project [24]. The application of the procedure to real-world scenarios will allow to refine both the design recommendations for MATE systems [1] and the evaluation procedure proposed in this paper.…”

Efficiency 4.0 for Industry 4.0

“…Smart machines can record sequences of human actions on machine interfaces to develop intelligent algorithms and, subsequently, reduce human workload in the production processes. Adaptive automation is the term used for this phenomenon (Kay, 2006;Villani et al, 2017).…”

“…Although CPPS machines are becoming more intelligent and may control and monitor the processes on their own by employing machine-learning algorithms , human interventions in production processes will still be required. Research in this area suggests that this monitoring of CPPSs will involve handheld mobile devices with rich interactive user interfaces (Meixner, Petersen, & Koessling, 2010) to monitor and operate the factory machines, often presenting complex information for real-time decisions (Villani et al, 2017;Zhong, Dai, Qu, Hu & Huang, 2013). This increased complexity in the data for humans in control operations will create increasing loads on the human information processing system; this information load in the brain is known as cognitive load (Sweller, 1988).…”