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With the development of human-cyber-physical-production systems in intelligent manufacturing, cyber-supported production based on artificial intelligence is becoming an increasingly powerful means of controlling machines and collaborating with human users. Semi-autonomous systems with a medium degree of automation enable human-centered, flexible, and sustainable production, for instance, in hybrid decision-making. Especially in applications that do not meet the requirements for full automation and when humans are to be involved in their role as qualified decision-makers, teaming-capable systems are desirable and offer considerable advantages. This paper outlines the transdisciplinary concept of human–machine teaming and the role of human cognition in engineering tasks with multi-criteria decision-making. An illustrative real-life example from thermal spray technology is used to show how explainable artificial intelligence models offer targeted, hybrid cyber decision support. This new approach based on fuzzy pattern classifiers combines expert knowledge- and data-based modeling and enables a transparent interpretation of the results by the human user, as shown here using the example of test data from atmospheric plasma spraying. The method outlined can potentially be used to provide hybrid decision support for a variety of manufacturing processes and form the basis for advanced automation or teaming of humans and cyber-physical-production systems. [ABSTRACT FROM AUTHOR]

Modeling forms the basis for optimal control of complex technical processes in the context of industry 4.0 development and, hence, for high product quality as well as efficient production. For the mechanical joining process of self-pierce riveting with 11 input and 5 output variables, two modeling approaches based on (1) experimental data and (2) FEM computer simulation are outlined and performed. A physical modeling approach is ruled out due to the high problem dimensionality and complex nonlinear dynamic relationships between input and output variables. Alternatively, data-based approaches lead to Artificial Intelligence (AI) model designs. The experimental approach is cost- and resource-consuming; therefore, only a relatively small data set can be collected. Here, we present results from experimental trials that serve as representatives and are generalized by a description with high-dimensional parametric membership functions (fuzzification). The fuzzification procedure is also applied to the FEM computer simulation results. In principle, it can provide an arbitrarily large database. However, consequently, time- and computational effort increase considerably. Both data sets form the basis for parallel model building using the AI method of local fuzzy pattern models, which can be used to describe highly nonlinear input-output relationships by error-minimizing partitioning. Finally, the comparison of the results of the two modeling approaches is outlined. Finally, a coupled modeling strategy and future model adaptation are proposed. [ABSTRACT FROM AUTHOR]