Towards Modern Inclusive Factories: A Methodology for the Development of Smart Adaptive Human-Machine Interfaces
This addresses the issue of operator strain and inefficiency in modern factories, but it is incremental as it builds on existing adaptive interface concepts.
The paper tackles the problem of complex human-machine interfaces (HMIs) causing high cognitive workload and training effort for operators in flexible manufacturing systems, proposing a methodology for adaptive HMIs based on measuring user capabilities, adapting information, and training to increase customization, productivity, and automation acceptance.
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.