Digital Twins are digital representations of products or product-service systems comprising a Digital Master, which consists of product description models, and a Digital Shadow, which encompasses data collected throughout the product’s life cycle. To create a Digital Twin, the Digital Master and Digital Shadow must be interlinked. The Digital Master, Digital Shadow, and thus their twinning can vary in complexity and analytical capabilities. This paper introduces a systematic description of six twinning levels ranging from simple data exchange based on generic models to more complex forms targeting model parameter and Digital Twin goal optimization. The example of a valve is used for illustration. The presented description aids in understanding the potential of Digital Twins and serves as a guide to select appropriate twinning levels based on specific product requirements and use cases.

The overall quality of final Digital Twin (DT) solutions and their ability to produce useful insights are key considerations for researchers and for the industry to readily adopt them. However, validation of DTs is often neglected in existing research dedicated to their development. Further, there is a lack of methodologies for building bi-directional information exchanges between virtual and real spaces, potentially hindering effective decision-making. This work presents a comparative analysis of several quantitative metrics by implementing them on the Digital Twin of a railway braking system as a use case. Their suitability as performance measures for validation and as thresholds to support decision-making is assessed. Their integration into a novel DT structure is shown to contribute to a well-rounded validation procedure and a practical decision-making framework.

To meet the upcoming sustainability challenges, aerospace manufacturers need to develop products that both address complex sustainability factors and ensure profitable realization. Furthermore, the sustainability perspective needs to be lifted from focusing on carbon emissions, and broadened to include a system-level socio-ecological view. Manufacturers are thus challenged to balance sustainability, manufacturability, and performance, but lack the methods and tools to make well-informed decisions. We propose a method for conducting multi-domain trade-off studies in the early design phase. A functional architecture modelling approach is utilized to model performance and manufacturing aspects. Together with a relative sustainability fingerprint conducted on design alternatives, design spaces can be explored with respect to performance, manufacturability, and sustainability.

Bolted joints are critical for maintaining structural integrity and reliability. Accurate prediction of parameters is essential for optimal performance. Traditional methods often fail to capture the non-linear behavior or require significant computational resources, limiting accuracy and efficiency. This study addresses these limitations by combining empirical data with a feed-forward neural network. Leveraging experimental data and systematic preprocessing, the model effectively captures nonlinear relationships, including rescaling output variables to address scale discrepancies, achieving 95% predictive accuracy. While limited dataset size restricts generalizability, the findings demonstrate the potential of neural networks as a reliable, efficient alternative for bolted joint design. Future work aims to expand datasets and explore hybrid modeling techniques to enhance applicability.

Engineering systems are represented in a variety of physical, graphical, and virtual ways, supporting decision making about the systems and their operation. As part of a larger research endeavor exploring influences of representation modality, the presented work examines how product identification impacts subsystem clustering behavior. This is achieved through a study using pictorial and functional representations of common household products. Participants were tasked with grouping elements into non-overlapping clusters. Results suggest that correctly identifying a product does not affect clustering behavior regardless of representation modality. This implies that other aspects of the representations are impacting partition convergence. These factors, along with connections to prior work are explored as discussion points and areas of future research.

Functional decomposition (FD) is essential for simplifying complex systems in engineering design but remains a resource-intensive task reliant on expert knowledge. Despite advances in artificial intelligence, the automation of FD remains underexplored. This study introduces the use of GPT-4o, enhanced with a proposed Monte Carlo tree search for functional decomposition (MCTS-FD) algorithm, to automate FD. The approach is evaluated qualitatively by comparing outputs with those of graduate engineering students and quantitatively by assessing metrics such as structural integrity and semantic accuracy. The results show that GPT-4o, enhanced by MCTS-FD, outperforms smaller models in error rates and graph connectivity, highlighting the potential of large language models to automate FD with human-like accuracy.

The main objective of this paper is the investigation of possibilities to enhance the resilience and sustainability of technical systems by means of function-oriented system design. Design for Resilience aims at creating technical systems capable of withstanding and adapting to internal and external changes. Design for Sustainability has the objective to create solutions that meet present needs without compromising future generations, for instance by means of avoiding environmental destruction, improving resource efficiency, and achieving a long-term ecological balance. Function-oriented design is the most abstract form of solution generation. This paper presents arguments to verify the hypothesis that function-oriented system design is a prerequisite for both Design for Resilience and Design for Sustainability, discusses connections between both aspects, and proposes a common process.

In the field of individualized medical implants for bone replacesment, additive manufacturing offers far-reaching advantages for bridging bone defects and supporting the production of natural form and function. The article uses the example of a large, customized cranial implant to show the challenges of manufacturing with osteoinductive bone cements. The process is shown, starting with planning and design, through to functional integration using adapted manufacturing strategies to create defined porosity.

The use of design methods across multiple design phases of the product development process often leads to inconsistency, the loss of transparency, and the rejection of design methods by practitioners. The authors of this work intend to develop a central modelling approach that supports consistency, based on the integrated function modelling (IFM) framework. Therefore, various design methods from the literature were examined for their techniques and content to identify indicators for supporting consistency. The results led to an enhancement of the IFM framework.

Decisions made in the concept generation phase have a significant effect on the product. While product- related risks typically can be considered in the early stages of design, risks such as supply chain and manufacturing methods are rarely easy to account for in early phases. This is because the currently available methods require mature data, which may not be available during concept generation. In this paper, we propose an approach to address this. First, the product and the non-product (manufacturing and/or supply chain) attributes are modelled using the enhanced function means (EF-M) modelling method. The EF-M method provides the opportunity to model alternative solutions-set for functions. Dependencies are then mapped within the product and the manufacturing models, and also in between them. An automatic combinatorial method of concept generation is employed where each generated instance is a design concept-manufacturing method pair. A risk propagation algorithm is then used to assess the risks of all the generated alternatives.

The transmission of information between requirements modelling and function modelling in the product development process often appears challenging because of multiple used models and different terminology of specific disciplines. The integrated function modelling (IFM) framework is used for functional analysis of technical moderate complex systems and supports cross-disciplinary modelling and communication in the design team. To improve the applicability of this method and its supporting purpose in the modelling process, the authors combined requirements as an additional entity with the existing entities of this method. Furthermore, the extended framework has been used to visualise the procedure with this approach as an example. The outlook provides the potential for further development of the method.

Lattice structures are lightweight and possess other unique mechanical and physical properties. Additive manufacturing techniques are often used to produce these structures. Additive manufacturing provides manufacturing freedom that significantly surpasses the one provided by conventional subtractive manufacturing. However, a gap exists between the manufacturing freedom and the geometric modelling freedom in additive manufacturing: it can be extremely challenging to model the designed part because of its high geometric complexity, such as heterogeneous lattice structures. While several tools on the market allow geometric modelling of such structures available on the market, the customization of lattice parameters can still be significantly improved. Moreover, no open-source tools exist to address this issue or to model lattice structures in general. This work presents a novel open-source library for the geometric modelling of lattice structures with customized parameters. The parameter customization is enabled with the function representation approach.

In the last few years, the growing need of highly reliable and time-effective strategies to perform preliminary design of complex systems has led industries to adopt the Model Based System Engineering (MBSE) approach. In MBSE, systems are split into multiple sub-systems and the relevant physical phenomena are described via analytical or numerical models. When a significant number of design variables are to be considered, a smart approach to reduce the number of analyses to perform would be to make use of the Global Sensitivity Analysis (GSA) to higlight those variables that have a more significant influence on the system output. Moreover, an even more significant reduction of computational cost to perform the GSA can be achieved if the complex system modelled via the MBSE approach is exported under the Functional Mock-Up Interface (FMI) norm. In this context, this paper proposes an original approach to address the study of two constructive solutions of an acceleration measuring device typically used on airbags for which the use of a new solution characterized by a porous material is compared with a classical one.

Due to an expanding number of mechatronic functionalities in modern technical products, the proportion of software and electronic components is also increasing. As a result, the products are developed by different engineering domains in complex development processes. To handle the growing complexity, Systems Engineering (SE) is increasingly important for development organizations of enterprises. System Engineering (SE) is understood as an approach to network the individual engineering domains and shall lead to a collaborative development of complex systems. Model-Based System Engineering (MBSE) expands SE by using common models and software tools to describe und visualize the systems. However, MBSE is not widely established in enterprises today. On the one hand, the introduction requires a distinct and consistent system understanding and collaborative way of working. On the other hand, the application of the existing tools requires extensive tool competencies due to many possible functions and features. Therefore, this paper presents a concept and a software based tool for a lean implementation of SE/MBSE to support the collaborative development of complex technical systems in small and medium-sized enterprieses.

Model-Based Systems Engineering (MBSE) is increasingly used across industries for the integrated modelling of complex systems to support model-based development and provide enhanced traceability between requirements and verification and validation of the system. This paper seeks to strengthen the function modelling methodology in MBSE by introducing an approach based on flow heuristics guided by the System State Flow Diagram schema. This provides function representations with an enhanced integrity in MBSE facilitating the solution-agnostic architecture modelling, and supports integrated simulation and function failure reasoning based on MBSE. The approach is illustrated with a case study of an electric bicycle implemented in the MathWorks System Composer environment.

This paper presents an evaluation study for the System State Flow Diagram function modeling framework based on a large-scale study with an automotive OEM. Technical reports are used to evaluate the usage of the framework within the organization. The paper also introduces a list of the type of problems that engineers are interested in in practical function modelling. The findings suggest that there is a widespread uptake of the framework across the organization and it supports the usage of relevant key engineering tools within the context of a broader model-based Failure Mode Analysis methodology.

The main focus of this paper is the exploration of fault accommodation possibilities in the context of function development. Faults occur in complex technical systems and may lead, if no accommodation entities or processes are present, to catastrophic failure. Several entities and processes exist and are applied, but mainly on the concrete levels. Faults very often concern more than one physical domain and accommodation possibilities are present in many physical or even non-physical domains. This paper explores this specific challenge and proposes an initial collection of countermeasures.

This paper investigates the use of ANNs to model human behaviour in design by comparing the predictive capability of ANNs and engineering students. Function structure models of 15 products are used as input for prediction. The type of information provided varied between topology and vocabulary. Analysis of prediction accuracy showed that ANNs perform comparably to students. However, students are more precise with their predictions. Finally, limitations and future work are discussed, with research questions presented for subsequent research.

This paper presents the re-design approach of an urban motorcycle helmet to prevent users bypassing the strap fastening system. Related studies show that although a full-face helmet provides the maximum protection to a rider, in practice, full-face helmeted riders in urban traffic tend to improperly fasten it. On that notion, the design goal was to conceive a helmet that combines the advantages of different helmet types while responding to urban driving needs. During design ideation possible solutions were examined focusing on different ways of accessing and fixating the helmet on a rider’s head, without using a strap fastening system. Preliminary concept development produced three design concepts, that were evaluated using two sets of prototypes: (a) the 3D printing method under a 1:2 scale was used to detect any design faults, while the 3D modeled concepts were evaluated in four different crash impacts regarding total deformation and von-Mises stress, and (b) 1:1 models of the three concepts were used by experienced riders to assess possible usability issues during helmet placement/removal. Results of the two-phase evaluation of the three concepts and design issues for further development of them are discussed.

The design of ITER, a large-scale nuclear fusion reactor, is intertwined with profound research and development efforts. Tough problems call for novel solutions, but the low maturity of those solutions can lead to unexpected problems. If designers keep solving such emergent problems in iterative design cycles, the complexity of the resulting design is bound to increase. Instead, we want to show designers the sources of emergent design problems, so they may be dealt with more effectively. We propose to model the interplay between multiple problems and solutions in a problem network. Each problem and solution is then connected to a dynamically changing engineering model, a graph of physical components. By analysing the problem network and the engineering model, we can (1) derive which problem has emerged from which solution and (2) compute the contribution of each design effort to the complexity of the evolving engineering model. The method is demonstrated for a sequence of problems and solutions that characterized the early design stage of an optical subsystem of ITER.