The development of mechatronic systems has always been characterized by continuous handling of uncertainties. This challenge, which is associated with dynamic changes in the development context, is increasingly met by companies in the development of physical systems with the implementation of agile approaches in their development processes. However, since established approaches have their origin in software development, they reach various limits in the context of the development of mechatronic systems, e.g. due to the physical properties of the systems. Other features, such as transparent and flexible project management or targeted and early involvement of customers and users in development processes, can also be implemented in mechatronic system development. In order to derive the potentials and limits of existing agile approaches for the context of mechatronic system development, the present paper compares existing approaches with regard to relevant factors from the context of mechatronic system development. The aim is to create a basis for the targeted development, adaptation and use of agile approaches in the field of mechatronic system development.

As additive manufacturing (AM) continues to grow in its abilities, so does the need for a quick and effective method of determining how it should be applied. Over time, these methods are naturally developed and passed on as tacit knowledge. However, with the rapid advancement of AM technologies, identifying parts which are eligible for AM as well as gaining insight on what value it may add to a product needs to be modelled in an objective and transferrable way. This paper presents a framework for determining the candidacy of a part or assembly for AM, represented by its economic feasibility and potential for AM-specific benefits. A set of selection criteria is developed with the goal of fast-screening in mind; that is specific data which can be automatically extracted from CAD models and resource planning databases. A case study is performed to validate the criteria and decision model chosen, as well as gain insight to the potential for a more widespread application. The decision model successfully identified economic feasibility and AM potentials, which suggests the results of the case study show promise for a semi-automatic decision support system for identifying AM candidates.

To meet rising customer requirements, increasingly complex products have to be virtually validated. To achieve this within the framework of virtual product development, a wide range of aspects has to be taken into account. In this context, tolerance analysis has established itself as a proven tool to evaluate the consequences of geometric part deviations on geometric product characteristics. Existing approaches, however, do not sufficiently take into account production-specific deviations, leading to time-consuming iterations during the product development process. Therefore, the focus of this contribution is on process-oriented interdisciplinary tolerance management that allows the integration of manufacturing simulations into the tolerance analysis. In contrast to the conventional approach, this novel methodology allows to avoid unnecessary iterations in the context of product development and validation. Following the presentation of the novel procedure, the application on a case study of an X- ray shutter is carried out, whereby surrogate models are integrated in order to reduce the computing time.

With the advance of the Internet and the Internet of Things, an abundance of 'big' data becomes available. Data science can be incorporated in design, which brings forward various opportunities for designers to benefit from this new material. However, the designer's perspective and their role remains unclear. How do they think about and approach data? What do they want to achieve with this data? What do they need to take ownership of designing with data? In this paper we take a design perspective to map the opportunities and challenges of leveraging large data-sets as part of the design process. We rely on a survey with 75 participants across a Faculty of Industrial Design Engineering and in-depth reflective interviews with a subset of 9 participants. We discuss the impact of data on the roles designers can adopt as well as an approach to designing with data. This work aims to inform on educational support, data literacy and tools needed for designers to take advantage of this new era of design digitalisation.

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.

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.

There are currently limited guidelines on how to design complex assistive technologies (ATs), which necessitates expertise beyond that possessed by designers, occupational therapists (OTs), or end-users. To address this issue, we conducted a series of four participatory workshops to study various configurations of OT-designer-user collaboration in co-designing do-it-yourself (DIY) ATs for an older adult with mobility impairment. We then proposed a specific co-design framework for such OT-designer-user collaboration.

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.

Planned obsolescence can artificially increase sales by stimulating desire or perceived need. This can be done in many ways and some companies are releasing newer models sooner than necessary or engineering the product to fail after a certain amount of use. In recent years, we have observed a change in the pattern of planned obsolescence strategies employed by technological companies, shifting from aesthetic to technological obsolescence. The reaction to this model comes from social enterprises and grassroots movements addressing the circular economy and repairability. This paper illustrates these relationships in context, taking the mobile phone industry as a case study. We focus on product architecture and product features, as a reference point to discuss the embodiment of strategies, and the degree of control the consumer is given for repairing the product. Using netnography as a method to collect data in a digital-mediated environment, this paper highlights how planned obsolescence strategies are embedded in product characteristics and summarises their evolution. It concludes by opposing planned obsolescence strategies to circular economy principles to discuss more sustainable pathways for the smartphone industry.

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.

Process models are among the principal artefacts used for managing design projects. However, the selection of effective modelling approaches can be difficult for design project managers, given that a plethora of tools exists for various modelling purposes. In addition to date no systematic approach for the assessment and selection of process modelling approaches is available to practitioners. This paper presents the development of criteria for benchmarking and selecting different process modelling tools. The results are based on three elements. (1) In a four-hour workshop undertaken by the Design Process SIG of the Design Society, bringing together around 20 international researchers and practitioners in design process modelling, an initial set of 58 criteria were brainstormed and consolidated during the workshop and in follow-up meetings. (2) The consolidated criteria were then compared with literature. The finalised criteria list was then validated by external experts in industry (3). The resulting list of 12 criteria provides a sound basis for practitioners to support a systematic selection of process modelling approaches. Further, it lays the foundation of a benchmarking tool, which is subject to future work.

Additive Manufacturing (AM) is a potentially revolutionary technique in industry with claims of high skills shortage in the recent days. It is assumed that full exploitation of AM capabilities can be made possible by a paradigm shift steered by engineering design. Future generations of engineers might benefit from Design for Additive Manufacturing (DfAM), which targets AM potential and enables design freedoms. In this context, the paper investigates AM education for a better understanding of the main AM-related subjects taught in universities. To this scope, the authors gathered 52 syllabi of courses taught in highly-ranked technical universities worldwide that relate to AM. From the investigation, it emerges that AM is the core discipline of the course in 42 out of 52 cases and considered widely as an independent domain to date. As for taught subjects, it was found that design aspects in AM and DfAM are poorly focused on, while manufacturing and process areas are the most popular. This poses a challenge especially to the design community, as the current situation might limit the exploitation of AM capabilities.

The Engineering Design field is growing fast and so is growing the number of sub-fields that are bringing value to researchers that are working in this context. From psychology to neurosciences, from mathematics to machine learning, everyday scholars and practitioners produce new knowledge of potential interest for designers.

This leads to complications in the researchers’ aims who want to quickly and easily find literature on a specific topic among a large number of scientific publications or want to effectively position a new research.

In the present paper, we address this problem by using state of the art text mining techniques on a large corpus of Engineering Design related documents. In particular, a topic modelling technique is applied to all the papers published in the ICED proceedings from 2003 to 2017 (3,129 documents) in order to find the main subtopics of Engineering Design. Finally, we analyzed the trends of these topics over time, to give a bird-eye view of how the Engineering Design field is evolving.

The results offer a clear and bottom-up picture of what Engineering design is and how the interest of researchers in different topics has changed over time.

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.

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.

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.

By applying data analytics to product usage information (PUI) from combinations of different channels, companies can get a more complete picture of their products’ and services’ Mid-Of-Life. All data, which is gathered within the usage phase of a product and which relates to a more comprehensive understanding of the usability of the product itself, can become valuable input. Nevertheless, an efficient use of such knowledge requires to setup related analysis capabilities enabling users not only to visualize relevant data, but providing development related knowledge e.g. to predict product behaviours not yet reflected by initial requirements.

The paper elaborates on explorations to support product development with analytics to improve anticipation of future usage of products and related services. The discussed descriptive, predictive and prescriptive analytics in given research context share the idea and overarching process of getting knowledge out of PUI data. By implementation of corresponding features into an open software platform, the application of advanced analytics for white goods product development has been explored as a reference scenario for PUI exploitation.

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.

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.

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.