Analyzing value creation and capture mechanisms of open source hardware startup companies, this paper illustrates how an open source strategy can make economical sense for hardware startups. By interviewing 37 open source hardware company leaders, 12 company community members as well as analyzing forum data of 3 open source hardware companies; we realize that by open sourcing the design of hardware, a company can naturally establish its community, which is a key element for a company's success. Establishing a community can increase customer perceived value, decrease product development and sales cost, shorten product go-to-market time, and incubate startups with knowledge, experience and resources. These advantages can compensate for the risks associated with open source strategies and can make open source design a viable product development strategy for hardware startups.

The emerging field of biomimicry and learning to design with and for nature has expanded in recent years through a diversity of educational programs. Inspiration following natural forms may give the appearance of being sustainable, but the question remains, how sustainable is it? Misunderstanding the function of these forms may leave designers with products not as sustainable as desired. Biomimicry education addresses these issues by integrating three essential elements into their design thinking phases and by using analogical transfer while doing so. This field learns from nature as model, nature as measure, and nature as mentor, throughout the design process. Through examination, analyses and verification of students designs and reflective processes at The Hague University of Applied Sciences, this research considers natures analogies in educational factors, determining which elements are influential when incorporating biomimicry into design education.

Conceptual design, as an early phase of the design process, is known to have the highest impact on determining the innovation level of design results. Although many tools exist to support designers in conceptual design, additional knowledge, especially knowledge related to emerging technologies, is still often needed. In this paper the authors aim to propose a data-driven creative concept generation and evaluation approach to support designers in incorporating emerging technologies in the new product early development stage. The approach is demonstrated by means of an illustrated example.

New tools from neuroscience allow design researchers to explore design neurocognition. By taking the advantage of EEG's temporal resolution we give up spatial resolution to focus on the performance of time-related design tasks. This paper presents results from an experiment using EEG to measure brain activation to study mechanical engineers and architects to compare their design neurocognition. In this study, we adopted and extended the tasks described in a previous fMRI study of design neurocognition reported in the literature. The block experiment consists of a sequence of 3 tasks: problem solving, basic design and open design using a physical interface. The block is preceded by a familiarizing pre-task using the physical interface and then extended to a fourth task using free-hand sketching. Brainwaves were collected from both mechanical engineers and architects. Results comparing 36 mechanical engineers and architects while designing were produced. These results indicate design cognition differences between the two domains in task-related power between the problem-solving task and the design tasks, in temporal resolution and transformed power.

A neurointerventional training model called HANNES (Hamburg ANatomical NEurointerventional Simulator) has been developed to replace animal models in catheter-based aneurysm treatment training. A methodical approach to design for mass adaptation is applied so that patient-specific aneurysm models can be designed recurrently based on real patient data to be integrated into the training system.

HANNES’ modular product structure designed for mass adaptation consists of predefined and individualized modules that can be combined for various training scenarios. Additively manufactured, individualized aneurysm models enable high reproducibility of real patient anatomies. Due to the implementation of a standardized individualization process, order-related adaptation can be realized for each new patient anatomy with modest effort. The paper proves how the application of design for mass adaptation leads to a well-designed modular product structure of the neurointerventional training model HANNES, which supports quality treatment and provides an animal-free and patient-specific training environment.

Digital inclusion is becoming more important as many consumer products and engineered systems adopt increasingly digital interfaces. The designers of such services often assume that users have a certain level of digital interface competence, but this is not the case for many users. In this paper, we present a set of personas that could help designers to better understand and consider the range of digital expertise across the population. The personas are based on survey data from 338 people across England and Wales in 2019. The survey examined various factors that affect ability to use digital interfaces, including technology experience, attitudes towards technology and competence with basic interface operations. Twelve clusters were identified using K-means cluster analysis, and twelve personas were developed based on these. The personas help to bring to life the range of digital expertise and highlight people at risk of digital exclusion. In addition, the cluster sizes indicate the proportion of the population represented by each persona and thus the scale of potential digital exclusion.

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.

How to enhance creativity, especially by applying new technologies to creativity methods, is a question posed continuously by researchers. One reason for this is that creativity is an important part of people's daily lives and an essential component of society. Therefore, many methods to enhance creativity have been created, especially brainstorming, which is one of the most popular and effective tools that inspire individuals to generate ideas, hence enhancing creativity. Moreover, technologies, such as virtual reality (VR), provide an opportunity for individuals and groups to be creative. In response, recent studies have adopted VR in brainstorming to enhance creativity. However, there is a lack of systematic analysis of experimental approaches and creativity measures employed in this context. Addressing this question, this study categorized existing articles on the topic related to categories of avatars, environments, interfaces, or applications. The findings elaborate on trends, measures employed to evaluate creativity and idea generation, identified categories, and results of these studies.

Artificial Intelligence (AI) has expanded in a diverse context, it infiltrates our social lives and is a critical part of algorithmic decision-making. Adopting AI technology, especially AI-enabled design, by end users who are non-AI experts is still limited. The incomprehensible, untransparent decision-making and difficulty of using AI become obstacles which prevent these end users to adopt AI technology. How to design the user experience (UX) based on AI technologies is an interesting topic to explore.

This paper investigates how non-AI-expert end users can be engaged in the design process of an AI-enabled application by using a framework called Smart Service Blueprint Scape (SSBS), which aims to establish a bridge between UX and AI systems by mapping and translating AI decisions based on UX. A Dutch mobility service called ‘stUmobiel ’ was taken as a design case study. The goal is to design a reservation platform with stUmobiel end users. Co-creating with case users and assuring them to understand the decision-making and service provisional process of the AI-enabled design is crucial to promote users’ adoption. Furthermore, the concern of AI ethics also arises in the design process and should be discussed in a broader sense.

Knowledge-based engineering (KBE) systems allow an easy adaption of designed artefacts to new functional or design requirements and automating routine design tasks. In the following article the author wants to focus on the three main concepts of linking CAD and KBE and answer the research questions (1) in which way is integration, embedding and coupling of KBE to a standard CAD system like Autodesk Inventor available and (2) how can the single approaches be compared in terms of modelling effort, user competences and system performance.

This paper builds a patent-based knowledge graph, patent-KG, to represent the knowledge facts in patents for engineering design. The arising patent-KG approach proposes a new unsupervised mechanism to extract knowledge facts in a patent, by searching the attention graph in language models. The extracted entities are compared with other benchmarks in the criteria of recall rate. The result reaches the highest 0.8 recall rate in the standard list of mechanical engineering related technical terms, which means the highest coverage of engineering words.

Cyber-physical systems (CPS) are able the collect huge amounts of data about themselves, their users, and their environment during their usage phase. By feeding these usage data back into product planning, manufacturers can optimize their engineering and decision-making processes. Despite promising potentials, most manufacturers still do not analyze usage data within product planning. Also, research on usage data-driven product planning is scarce. Therefore, this paper aims to identify the main concepts, advantages, success factors and challenges of usage data-driven product planning. To answer the corresponding research questions, a comprehensive systematic literature review is conducted. From its results, a detailed description of usage data-driven product planning consisting of six main concepts is derived. Furthermore, taxonomies for the advantages, success factors and challenges of usage data-driven product planning are presented. The six main concepts and the three taxonomies allow for a deeper understanding of the topic while highlighting necessary future actions and research needs.

Machinery and plant engineering in Germany is characterized by small and medium-sized enterprises. The so-called backbone of German industry is in transition towards Industry 4.0, with systems becoming more complex and the development task becoming an interdisciplinary task. Systems Engineering is a proven approach to realize these systems. Projects with SE approaches were accompanied and potentials of SE were structured. In this paper, we discuss the need for action for the company-wide introduction and present a solution concept.

Generative AI algorithms that are able to generate creative output are progressing at tremendous speed. This paper presents a research agenda for Generative AI-based support for designers. We present examples of existing applications and thus illustrate the possible application space of Generative AI reflecting the current state of this technology. Furthermore, we provide a theoretical foundation for AI-supported design, based on a typology of design knowledge and the concept of evolutionary creativity. Both concepts are discussed in relation to the changing roles of AI and the human designer. The outlined research agenda presents 10 research opportunities for possible AI-support to augment the designer of the future. The results presented in this paper provide researchers with an introduction to and overview of Generative AI, as well as the theoretical understanding of potential implications for the future of the design discipline.

Recently, design researchers have begun to use neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) to understand a variety of cognitive processes relevant to design. However, common neuroimaging analysis techniques require significant assumptions relating temporal and spatial information during model formulation. In this work, we apply hidden Markov Models (HMM) in order to uncover patterns of brain activation in a design-relevant fMRI dataset. The underlying fMRI data comes from a prior research study in which participants generated solutions for twelve open-ended design problems from the literature. HMMs are generative models that are able to automatically infer the internal state characteristics of a process by observing state emissions. In this work, we demonstrate that distinct states can be extracted from the design ideation fMRI dataset, and that designers are likely to transition between a few key states. Additionally, the likelihood of occupancy within these states is different for high and low performing designers. This work opens up the door for future research to investigate the patterns of neural activation within the discovered states.

This paper presents an EEG (Electroencephalography) study that explores correlations between the neurophysiological activations, the nature of the design task and its outputs. We propose an experimental protocol that covers several design-related tasks: including fundamental activities (e.g. idea generation and problem-solving) as well as more comprehensive task requiring the complex higher-level reasoning of designing. We clustered the collected data according to the characteristics of the design outcome and measured EEG alpha band activation during elementary and higher-level design task, whereas just the former yielded statistically significant different behaviour in the left frontal and occipital area. We also found a significant correlation between the ratings for elementary sketching task outcomes and EEG activation at the higher-level design task. These results suggested that EEG activation enables distinguishing groups according to their performance only for elementary tasks. However, this also suggests a potential application of EEG data on the elementary tasks to distinguish the designers' brain response during higher-level of design task.

A prototype can be generally defined as a preliminary version of a final product and it can represent both aesthetic and functional features. Prototyping, the process of building a prototype, constitutes an indispensable part of product development processes. Many classifications of prototypes which use a different range of criteria have already been developed and are known as Prototype Taxonomies. This paper proposes an agenda towards the development of a new taxonomic classification which will be based on a prototype's attributes and its intended purpose. This work is justified from the results of an analysis of 8 existing prototype taxonomies which indicate that there is not a taxonomy that explicitly uses the prototypes’ purposes as its basic taxonomic dimension. A definition of the term ‘Prototype Purpose’ is proposed through discussing the differences and relations to the term ‘Prototype Role’. This work results in a list of 23 explicit prototype purposes which arise from 7 roles of prototyping found in relevant literature: Learning, Communication, Demonstration, Integration, Refinement, Exploration and Requirement Elicitation.

Design is a complex problem-solving activity that transforms design restrictions and requirements into a set of constraints and explores the feasible solutions to satisfy those constraints. However, design solutions generated by traditional modeling approaches are hardly to deal with such constraints, particularly for the exploration of the possible design solution space to enhance the quality of the design outputs and confront the evolving design requirements. In this regard, the Generative Design Approach (GDA) is considered as an efficient method to explore a large design solution space by transforming the design problem into a configuration problem. Fundamentally, GDA explores and stores all the necessary knowledge through a design skeleton and a set of design elements. Thus, design solution space is easily explored by configuring variable design elements via iterative design processes. Further, the output model is not only a design solution but also a design concept that designers could manipulate to explore unconsidered design configurations. Finally, a crank creation as a running example confirmed that GDA provides concrete aids to enhance the diversity of design solutions.

Additive manufacturing offers several potentials such as the freedom of design, part consolidation, function integration, or time and cost-savings. These potentials make AM interesting for industries such as aerospace, automotive and medical implants, and are also seen as enables for the creation of entirely new business models. Additive manufacturing has the potential to change the current manufacturing landscape substantially and has attracted much attention of industry and academia over the last decades.

However, these developments require improvements concerning the technology itself and its successful implementation into the value creation chain. Driven by the promising market opportunities and upcoming technological developments, many research activities started.

This paper presents a literature review of publications from the last 20 years. Based on this analysis, the evolution of the AM research landscape is portrayed. The research landscape is organised into four areas: machine and process, material, digital process chain and methodology. The paper summarises developments in each of these areas and concludes by presenting current and discussing future research topics.

Artificial intelligence is transforming how we interact with vehicles. We examine the case of Maritime Autonomous Surface Ships (MASS), which are emerging as a safer and more effective solution for maritime transportation. Despite the focus on autonomy, humans are predicted to have a central role in MASS operations from a Shore Control Centre (SCC). Here, operators will provide back-up control in the event of system failure. There are signification design challenges with such a system. The most critical is human-system interaction in autonomy (H-SIA). We consider humans as the source of resilience in the system for adapting to unexpected events and managing safety. We ask, can Human-Centred Design (HCD) be used to create resilient interactions between MASS and SCC? Work has been done in resilience engineering for complex systems but has not been extended to H-SIA in transportation. “Resilient interaction design” is relevant as we progress from design to operational phase. We adopted the ISO 9421-210 guideline to structure our HCD approach. The result is an SCC designed for 1 Autonomy Operator (AO). The contribution is a demonstration of how resilient interaction design may lead to safer and more effective H-SIA in transportation.