Evaluation of Cognitive Architectures for Cyber-Physical Production Systems

Cyber-physical production systems (CPPS) integrate physical and computational resources due to increasingly available sensors and processing power. This enables the usage of data, to create additional benefit, such as condition monitoring or optimization. These capabilities can lead to cognition, such that the system is able to adapt independently to changing circumstances by learning from additional sensors information. Developing a reference architecture for the design of CPPS and standardization of machines and software interfaces is crucial to enable compatibility of data usage between different machine models and vendors. This paper analysis existing reference architecture regarding their cognitive abilities, based on requirements that are derived from three different use cases. The results from the evaluation of the reference architectures, which include two instances that stem from the field of cognitive science, reveal a gap in the applicability of the architectures regarding the generalizability and the level of abstraction. While reference architectures from the field of automation are suitable to address use case specific requirements, and do not address the general requirements, especially w.r.t. adaptability, the examples from the field of cognitive science are well usable to reach a high level of adaption and cognition. It is desirable to merge advantages of both classes of architectures to address challenges in the field of CPPS in Industrie 4.0

Seeing the woods for the trees: the problem of information inefficiency and information overload on operator performance

One of the recurring questions in designing dynamic control environments is whether providing more information leads to better operational decisions. The idea of having every piece of information is increasingly tempting (and in safety critical domains often mandatory) but has become a potential obstacle for designers and operators. The present research study examined this challenge of appropriate information design and usability within a railway control setting. A laboratory study was conducted to investigate the presentation of different levels of information (taken from data processing framework, Dadashi et al., 2014) and the association with, and potential prediction of, the performance of a human operator when completing a cognitively demanding problem solving scenario within railways. Results indicated that presenting users only with information corresponding to their cognitive task, and in the absence of other, non task-relevant information, improves the performance of their problem solving/alarm handling. Knowing the key features of interest to various agents (machine or human) and using the data processing framework to guide the optimal level of information required by each of these agents could potentially lead to safer and more usable designs

Exploring the integration of the human as a flexibility factor in CPS enabled manufacturing environments: methodology and results

Cyber Physical Systems (CPS) are expected to shape the evolution of production towards the fourth industrial revolution named Industry 4.0. The increasing integration of manufacturing processes and the strengthening of the autonomous capabilities of manufacturing systems make investigating the role of humans a primary research objective in view of emerging social and demographic megatrends. Understanding how the employees can be better integrated to enable increased flexibility in manufacturing systems is a prerequisite to allow technological solutions, as well as humans, to harness their full potential. Humans can supervise and adjust the settings, be a source of knowledge and competences, can diagnose situations, take decisions and several other activities influencing manufacturing performances, overall providing additional degrees of freedom to the systems. This paper, studies two different integration models: Human-in-the-Loop and Human-in-the-Mesh. They are both analysed in the context of four industrial cases of deployment of cyber physical systems in production