The paper explores and comments on the theoretical concept of human-machine-teaming in intelligent manufacturing. Industrial production is an important area of work applications and should be developed toward a more anthropocentric Industry 4.0/5.0. Teaming is used a design metaphor for human-centered integration of workers and complex cyber-physical-production systems using artificial intelligence. Concrete algorithmic solutions for technical processes should be based on theoretical concepts. A combination of literature scoping review and commentary was used to identify key characteristics for teaming applicable to the work environment addressed. From the body of literature, five criteria were selected and commented on. Two characteristics seemed particularly promising to guide the development of human-centered artificial intelligence and create tangible benefits in the mid-term: complementarity and shared knowledge/goals. These criteria are outlined with two industrial examples: human-robot-collaboration in assembly and intelligent decision support in thermal spraying. The main objective of the paper is to contribute to the discourse on human-centered artificial intelligence by exploring the theoretical concept of human-machine-teaming from a human-oriented perspective. Future research should focus on the empirical implementation and evaluation of teaming characteristics from different transdisciplinary viewpoints.

Introduction: Digitalization in intelligent manufacturing leads to the development of Industry 4.0/5.0 and human-cyber-physical systems. As many production technologies rely on teaming of human workers and intelligent cyber-physical systems such as industrial robots, human-robot collaboration is an intensively investigated topic in this transdisciplinary research area. To design industrial robots in a human-centered way, psychological knowledge concerning judgment and decision-making needs to be gained and integrated.Method: This paper reports results from an experimental study (N = 222, 2 × 4 within-subjects design) using eight moral dilemmas framed in the context of human-robot-collaboration to examine the influence of spatial distance of an industrial robot and humans (no direct contact, different tasks vs. no direct contact, same task vs. handing-over contact, same task vs. direct contact, same task) on moral decisions. Additionally, the type of dilemma was varied, with every four dilemmas depicting a life-or-death and an injury scenario. Participants responded on a four-point-response scale which actions they would take indicating deontological or utilitarian moral decision-making.Results: Results show a large effect of the proximity of the cooperation between robots and humans. The closer the collaboration the more a human tends to choose utilitarian moral choices.Discussion: It is argued that this effect might stem from an adaptation of human rationality to the robot or overreliance and shift of responsibility to the robot team partner.

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.

When it comes to climate change, automated vehicles (AV) are often presented as a key factor to reducing emissions related with the transport sector. While studies promise emissions savings of up to 80%, it is often overlooked how AVs will be introduced and which transportation mode changes will arise from their implementation. Therefore, this online survey examined usage intentions regarding private and shared AV types, and underlying attitudes and mobility needs of 136 current users of different main modes of transport. Two main results counteract the general assumption of ecological sustainability benefits of AVs: First, current car drivers prefer private over shared AV types, even though notable sustainability gains can only be expected from shared AVs. Second, current users of more sustainable modes of transport (walking, bike, public transport) would replace theses modes by AVs for substantial shares of their trips, which represents a behavioural rebound effect, since AVs cannot be more sustainable than walking or biking. Group-specific mobility needs and knowledge gaps regarding the sustainability of different AV types are identified as reasons for these results and as starting points for deriving necessary measures accompanying the introduction of AVs into society through motivating ecologically sustainable transportation mode changes.

With the evolution of traditional production towards smart manufacturing, humans and machines interact dynamically to handle complex production systems in semi-automated environments when full automation is not possible. To avoid undesirable side effects, and to exploit the full performance potential of experts, it is crucial to consider the human perspective when developing new technologies. Specifically, human sub-tasks during machine operation must be described to gain insights into cognitive processes. This research proposes a cognition-based framework by integrating a number of known psychological concepts. The focus is on the description of cognitive (team) processes in the resolution of anomalies within a manufacturing process with interdisciplinary experts working together. An observational eye tracking study with retrospective think-aloud interviews (N = 3) provides empirical evidence for all cognitive processes proposed in the framework, such as regular process monitoring and—in case of a detected anomaly—diagnosis, problem solving, and resolution. Moreover, the role of situation awareness, individual expertise and (cognitive) team processes is analyzed and described. Further, implications regarding a human-centered development of future production systems are discussed. The present research provides a starting point for understanding and supporting cognitive (team) processes during intelligent manufacturing that will dominate the production landscape within Industry 5.0.

Many cyber-physical systems face the challenge of appropriately integrating domain-specific human expert knowledge into the cyber part to create a shared sphere of knowledge and intelligent interactions between humans and the semi-autonomous technical system. Cognitive engineering contributes methods and insights into higher-order cognition that help to embed human knowledge in an appropriate way. The original research introduces a novel transdisciplinary framework called Human-CoMo, which demonstrates a systematic modelling process, different human perspectives, and the integration of expert knowledge at multiple hierarchical levels. Fundamental principles inspired by human cognition, such as conceptual chunking and knowledge precision, are characterised. Furthermore, it is shown how knowledge hierarchies can be methodically reflected in appropriate data analysis and modelling levels for small and big data applications including artificial intelligence approaches. Combined knowledge- and data-based modelling approaches offer more flexibility to integrate the strengths of humans and technology in a complementary way. The cognitive foundations and their computational reflections are outlined for the technical example process electroplating from the field of materials and surface engineering. The possibilities and limitations of integrating human knowledge through formalisation and implications for future forms of human-machine interaction are discussed.