Cyber-Physical-Systems provide extensive data gathering opportunities along the lifecycle, enabling data-driven design to improve the design process. However, its implementation faces challenges, particularly in the initial data capturing stage. To identify those, a comprehensive approach combining a systematic literature review and an industry survey was applied. Four groups of interrelated challenges were identified as most relevant to practitioners: data selection, data availability in systems, knowledge about data science processes and tools, and guiding users in targeted data capturing.

The environment for powertrain production system engineers is changing radically. This initial prescriptive study proposes a systems engineering framework based on two previous case studies which are under review for publication concerning design of battery plants. The framework was developed based on ISO/IEC/IEEE 15288 standard using Concept of Operations and Model-Based Systems Engineering in a workshop setting, with a focus on visualisation to understand the practical and emotional needs of the humans in the system. The framework was validated by twelve senior project members.

Given the complexity of cyber-physical systems (CPS), such as swarms of drones, often deviations, from a planned mission or protocol, occur which may in some cases lead to harm and losses. To increase the robustness of such systems, it is necessary to detect when deviations happen and diagnose the cause(s) for a deviation. We build on our previous work on soft agents, a formal framework based on using rewriting logic for specifying and reasoning about distributed CPS, to develop methods for diagnosis of CPS at design time. We accomplish this by (1) extending the soft agents framework with Fault Models; (2) proposing a protocol specification language and the definition of protocol deviations; and (3) development of workflows/algorithms for detection and diagnosis of protocol deviations. Our approach is partially inspired by existing work using counterfactual reasoning for fault ascription. We demonstrate our machinery with a collection of experiments.

The new development of cyber-physical product families currently lacks a methodically supported modularisation approach. This paper provides an approach for module-based mechatronic development, which provides design for future product variety. The state of the art in terms of mechatronic system design and modular product architecture design is presented. A modified V-model is then shown that integrates initial product architecture design and life phase modularisation. The method is applied and evaluated for the development of product family generations of robot units in a teaching course.

One of the main current threats for producing companies is the possibility of cyber-space attacks. Due to several reasons, industrial companies need to connect their plants over some kind of networks. Obviously, today many approaches from information technology (IT) exist which will greatly reduce the danger of attacks. Still, no producing company can completely rely on these IT solutions. This paper proposes a systemic approach to design processes and products for increased cyber security. This approach is based on an attack model and is explained based on an automated storage system.

The goal in system-level design is to generate a diverse set of high-performing design configurations that allow trade-offs across different objectives and avoid early concretization. We use deep generative models to learn a manifold of the valid design space, followed by Monte Carlo sampling to explore and optimize design over the learned manifold, producing a diverse set of optimal designs. We demonstrate the efficacy of our proposed approach on the design of an SAE race vehicle and propeller.

The implementation of Digital Twins has become a common task for many industrial companies to ensure a sufficient digitization of their products and maintain competitiveness. This results in the question of how to compensate additional effort caused by designing Digital Twins. With this paper, an approach for this compensation is presented by creating Digital Twin behaviour through utilizing SysML diagrams and directly derivate usable code from them for a further implementation. This offers a part solution of lowering the threshold for using MBSE and increasing its benefits.

Machine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

The development of cyber-physical systems requires close cooperation between stakeholders from different disciplines. Model-based systems engineering support this by the design of a system model. Non-identified domain knowledge by the stakeholders is a challenge when creating the system model. The CONSENS 3D-Modeling Method supports the domain-independent elicitation of domain knowledge using a 3D environment and enables the derivation of a SysML system model. We applyed the method by implementing a prototype, called 3D Engineer, to an application example from the automotive industry.

The paper suggests an innovative design research and intervention approach using a poststructuralist organizational education perspective. The potential of a high impact trans-epistemic design process is shown for the field of industry 4.0 and the specific context of cognitive assistive systems (CASs). The multi-layered approach addresses the design of technical, social and educational complexity to implement CASs sustainably on the shopfloor and exploit their potential in industry 4.0. Finally, we will shed light on how the approach can enhance deep organizational transformation in industry.

This paper presents a test bed for AI technology on the integration of creative AI (CAI) with hybrid design tools (HDTs). The objective is to build and develop tools and programs for creative people (e.g. designers, engineers) to use, whereby the artificial intelligence (AI) software acts as a creative collaborator rather than a mere tool. The goal is to find a set of guiding principles, metaphors and ideas that inform the development of a CAI praxis imbued with computational support tools, new theories, experiments, and applications. Results and findings are presented of early-stage research.

With the introduction of ‘5G’ data transfer gets faster and further reaching than ever before. This new communication technology paves the way for an exchange of skills and competencies between humans and machines. This raises the question of how future users can profit and understand the potential brought about by these technologies. This paper elaborates the use of demonstrators in a pilot study as research tools and assesses their potential. It gives first insights why demonstrators are suitable to set a basis for public recognition for body-worn CPS and how to promote innovative visions.

Despite recent advancements on cloud-enabled and human-in-the-loop cyber-physical systems, there is still a lack of understanding of how infrastructure-related quality of service (QoS) issues affect user-perceived quality of experience (QoE). This work presents a pilot experiment over a cloud-enabled mobility assistive device providing a guidance service and investigates the relationship between QoS and QoE in such a system. In our pilot experiment, we employed the CloudWalker, a system linking smart walkers and cloud platforms, to physically interact with users. Different QoS conditions were emulated to represent an architecture in which control algorithms are performed remotely. Results point out that users report satisfactory interaction with the system even under unfavorable QoS conditions. We also found statistically significant data linking QoE degradation to poor QoS conditions. We finalize discussing the interplay between QoS requirements, the human-in-the-loop effect, and the perceived QoE in healthcare applications.