Alois Ferscha

Kashif Zia, Alois Ferscha, Dari Trendafilov

Manufacturing systems of the future need to have flexible resources and flexible routing to produce extremely personalized products, even of lot size equal to one. In this paper, we have proposed a framework, which is designed to achieve this goal. Towards this, we have integrated an established cultural evolution model to achieve desired flexibility of resources and acceptable routing time. Promising results are evidenced through a simple proof-of-concept simulation.

Juergen Mangler, Konrad Diwol, Dieter Etz et al.

Industrial Safety deals with the physical integrity of humans, machines and the environment when they interact during production scenarios. Industrial Safety is subject to a rigorous certification process that leads to inflexible settings, in which all changes are forbidden. With the progressing introduction of smart robotics and smart machinery to the factory floor, combined with an increasing shortage of skilled workers, it becomes imperative that safety scenarios incorporate a flexible handling of the boundary between humans, machines and the environment. In order to increase the well-being of workers, reduce accidents, and compensate for different skill sets, the configuration of machines and the factory floor should be dynamically adapted, while still enforcing functional safety requirements. The contribution of this paper is as follows: (1) We present a set of three scenarios, and discuss how industrial safety mechanisms could be augmented through dynamic changes to the work environment in order to decrease potential accidents, and thus increase productivity. (2) We introduce the concept of a Cognition Aware Safety System (CASS) and its architecture. The idea behind CASS is to integrate AI based reasoning about human load, stress, and attention with AI based selection of actions to avoid the triggering of safety stops. (3) And finally, we will describe the required performance measurement dimensions for a quantitative performance measurement model to enable a comprehensive (triple bottom line) impact assessment of CASS. Additionally we introduce a detailed guideline for expert interviews to explore the feasibility of the approach for given scenarios.