Having upended the traditional software development, which historically was centred exclusively on proprietary, copyright-protected code, open-source has now entered the physical artefact world. In doing so, it has started to change not only how physical products are designed and developed, but also the commercialisation process. In recent years, authors have witnessed entrepreneurs intentionally choosing not to patent their product design and technologies but instead licencing the designs and technologies under open-source licences. The entrepreneurs share their product designs online with their community – people who congregated due to the shared interests in products’ technology or project’s social objectives. Founding a startup firm without excluding others from using their own invention is not a common practice. Therefore, there is reason to ask if this choice a strategic decision or irrational action due to short-sightedness or extreme altruism? Conducting interviews with 65 founders, we grounded a framework explaining that the driver of going open is a result of both intrinsic and extrinsic factors. In addition, we observed the change of identities over time among the entrepreneurs. We hope to use this paper as a pilot study of this emerging socio-technological phenomenon, which is understudied relative to the proprietary product commercialisation process.

This paper presents a novel manufacturing technique for complex-shaped, hybrid metal composite structures leveraging the design freedom of additive manufacturing (AM). The key novelty of this research is an approach for an autoclave-suitable and removable tooling, which consists of a 3D-printed functional shell and a structural filler material. In this process, a layup shell is produced with AM and filled with a temperature-resistant curing support to form a removable inner tooling. The functional shell has integrated design features for the positioning and the fixation of metallic interface elements and is removed after curing through integrated breaking lines. The feasibility of this manufacturing technique is demonstrated by fabricating a novel lightweight structure for the hydraulic quadruped (HyQ) robot. Selective laser sintering (SLS) was used to produce the functional shell tooling. Titanium interface elements made via selective laser melting (SLM) were assembled to the shell and co-cured to carbon fiber using an autoclave prepreg process. The resulting multi-material structure was tested in ultimate strength and successfully operated on the HyQ robot. Weight savings of 55% compared to a reference design and the mechanical viability of the multi-material structure indicate that the proposed manufacturing technique is appropriate for individualized hybrid composite structures with complex geometries.

Injera is Ethiopian ethnic traditional staple food, made from teff and other crops. The country’s superfoods are highly regarded in many Western countries for their excellent nutritional properties (‘very low gluten’), and mineral composition. Injera provides sufficient conditions for fungi to grow and ruin injera by changing the texture and making it inedible. This study aimed to develop an evaporative cooler clay chamber (ECCC) used to extend the shelf life of injera by controlling temperature and relative humidity (RH). This system is an economical and efficient way to lower the temperature and increase RH. The results show the maximum daily ambient temperature reduced from 28.98 °C to 22.90 °C and increased the RH of the storage chamber from 28.78% to 80.94%, respectively. The temperature drops up to 6.08 °C, and the RH rises to 52.16% observed. Additionally, the effects of temperature, RH and potential of hydrogen were studied for identified rotten injera and designed for injera storage. ECCC can store freshly made injera for 9 days before any visible mould stains appear, significantly reducing weight loss with an average cooling efficiency of 79.31%. Therefore, this study might help to develop low-cost cold storage for injera preservation at farms end in Ethiopia.

A critical task in product design is mapping information from the consumer space to the design space. This process is largely dependent on the designer to identify and relate psychological and consumer level factors to engineered product attributes. In this way, current methodologies lack provision to test a designer’s cognitive reasoning and may introduce bias through the mapping process. Prior work on Cyber-Empathic Design (CED) supports this mapping by relating user–product interaction data from embedded sensors to psychological constructs. To understand consumer perceptions, a network of psychological constructs is developed using Structural Equation Modeling for parameter estimation and hypothesis testing, making the framework falsifiable in nature. The focus of this technical brief is toward automating CED through unsupervised deep learning to extract features from raw data. Additionally, Partial Least Square Structural Equation Modeling is used with extracted sensor features as inputs. To demonstrate the effectiveness of the approach a case study involving sensor-integrated shoes compares three models – a survey-only model (no sensor data), the existing CED approach with manually extracted sensor features, and the proposed deep learning based CED approach. The deep learning based approach results in improved model fit.

Over the past years, computational methods based on deep learning—that is, machine learning with multilayered neural networks—have become state-of-the-art in main research areas in computer-aided architectural design (CAAD). To understand current trends of CAAD with deep learning, to situate them in a broader historical context, and to identify future research challenges, this article presents a systematic review of publications that apply neural networks to CAAD problems. Research papers employing neural networks were collected, in particular, from CumInCad a major open-access repository of the CAAD community and categorized into different types of research problems. Upon analyzing the distribution of the papers in these categories, namely, the composition of research subjects, data types, and neural network models, this article suggests and discusses several historical and technical trends. Moreover, it identifies that the publications analyzed typically provide limited access to important research components used as part of their deep learning methods. The article points out the importance of sharing training experiments and data, of describing the dataset, dataset parameters, dataset samples, model, learning parameters, and learning results to support reproducibility. It proposes a guideline that aims at increasing the quality and availability of CAAD research with machine learning.

Interdisciplinary engineering of cyber physical production systems (CPPS) are often subject to delay, cost overrun and quality problems or may even fail due to the lack of efficient information exchange between multiple interdisciplinary teams working in complex networks within and across companies. We propose a direct integration of multiteam and organisational aspects into the graphical notation of the systems engineering workflow. BPMN++, with eight new notational elements and two subdiagrams, enables the modelling of the required cooperation aspects. BPMN++ provides an improved overview, uniform notation, more compact presentation and easier modifiability from an engineering point of view. We also included a first set of empirical studies and historical qualitative and quantitative data in addition to subjective expert-based ratings to increase validity. The use case introduced to explain the procedure and the notation is derived from surveys in plant manufacturing focussing on the start-up phase and decision support at site. This, in particular, is one of the most complex and critical phases with potentially high economic impact. For evaluation purposes, we compare two alternative solutions for a short-term management decision in the start-up phase of CPPS using the BPMN++ approach.

Idea selection is crucial in design as it impacts the outcome of a project. A collaborative design activity could be considered as a social process where the interactions and individual states (such as the importance in the team and self-efficacy level) could affect decision-making. It is often seen in design teams that some individuals, referred to as ‘influencers’ in the article have more capacity to influence than others, hence they govern the team process for better or worse. Due to the limited work done in the past to study the effect of these influencers on design outcomes, the work aims at increasing the understanding by presenting some insights from its agent-based simulation. The simulation results show how different influencer team compositions affect design outcomes in terms of quality and exploration of the solutions. The idea selection starts with the agents who are ready with their solution in their ‘mind’. The work presented in this article describes a framework for simulating decision-making during idea selection by considering the influencer and majority effect. The empirical study presented in the article verifies the model logic, that is, the presence of influencer and the majority during idea selection and supports the assumption that individuals’ agreement on solutions proposed by other team members depends on the degree of influence and past agreement. The results of the simulation show that teams with well-defined influencers produced solutions with higher variety and had more uniform contributions from team members, but also produced solutions of lower quality.

Along with the ever-increasing customer demands, early consideration of variation in terms of robust design is important to avoid expensive iterations in the product development. However, existing methods are detached from the development process and can therefore only be applied at a late stage or only with comprehensive expert knowledge. Especially in the concept stage, where the geometry of a product is not yet defined and the optimisation potential is high, effective solution proposals for systematic consideration of variation are lacking. Therefore, this paper describes a new integrated approach facilitating robust and tolerance design in the concept stage. The novelty of the approach using ontologies and graph-based visualisation is the close linkage of product development and tolerance knowledge, which allows automation and helps to avoid time- and cost-intensive iteration loops. As a result, a robust and tolerance-compliant concept design, an initial qualitative tolerance specification and instructions for the further tolerancing process are already available at the end of the concept stage. The applicability and the benefits of the approach are illustrated by representative case studies and a user study allowing a critical comparison between the conventional, mostly subjective procedure and the presented approach.

The problem at hand is that vast amount of data on industrial changes is captured and stored; yet the present challenge is to systematically retrieve and use them in a purposeful way. This paper presents an industrial case study where complex product development processes are modeled using the design structure matrix (DSM) to analyze engineering change requests sequences. Engineering change requests are documents used to initiate a change process to enhance a product. Due to the amount of changes made in different projects, engineers want to be able to analyze these change processes to identify patterns and propose the best practices. The previous work has not specifically explored modeling engineering change requests in a DSM to holistically analyze sequences. This case study analyzes engineering change request sequences from four recent industrial product development projects and compares patterns among them. In the end, this research can help to identify and guide process improvement work within projects.

Innovation Engineering (IE) is an educational training program that presents tools and advice on product innovation in three main categories: Create, idea generation; Communicate, communicating ideas; and Commercialize, selecting ideas to invest in further. The concepts taught in IE include common suggestions for early-stage product innovation. This paper addresses a challenge of implementing the IE program, specifically that it does not provide peer-reviewed sources or adequate data to substantiate its approach. This lack of substantiation limits effective implementation at companies. This paper also takes a step in examining IE’s claims that it is ‘a new science’ and a ‘new field of academic study’, a topic motivated by the Design Science Journal’s aim to serve as the archival venue of science-based design knowledge across multiple disciplines. This paper provides a compilation of academic literature that has tested the tools and advice espoused by IE. Almost all included papers contain test-versus-control experimental evidence. A mix of supporting and refuting evidence was found. Overall, the work provides a useful compilation of evidence-of-effectiveness related to common innovation and design practices that spans different design stages and is applicable for multiple disciplines and industries. This evidence comes from a variety of sources, including design, engineering education, psychology, marketing, and management. The work can also serve as an approach to evaluate overarching approaches to design in general, specifically, testing the foundations by vetting related test-versus-control experimental studies.

Lightweight design demands and complexity requirements of modern high-end structures in aerospace, automotive, sports and bioengineering can be successfully covered by a combination of fiber reinforced polymers (FRPs) with metallic components. Conventionally, mechanical locking is favored in integrating multi-material parts, avoiding bonded interfaces. The feasibility of a multi-material carbon FRP–aluminum structural component of a robotic exoskeleton, fabricated in a single step with the FRP directly cured on the aluminum domain, was investigated. To conduct the feasibility analysis, pertinent systematic FE modeling involving cohesive contact was employed to optimize the design, while strength and fracture testing were conducted to define the formed interfaces’ resistance. Sandblasting treatment was also investigated and compared with plain surfaces. The results show that the effect of residual stresses due to curing process governs the created joint’s durability. To reduce their effect, the local compliance of the multi-material components was altered by introducing a compliant layer along with modification of the aluminum domains’ local geometry in a manner that does not compromise the overall structural integrity. The interface stresses of the optimized geometry are a few times lower than the ones estimated for the initial design. The methodology adopted herein delivers some guidelines on treating such problems.

In the design of long fibre reinforced thermoplastic (LFT) structures, there is a direct dependency on the manufacturing. Therefore, it is indispensable to integrate the manufacturing influences into the design process. This not only offers new opportunities for material- and load-adapted designs, but also reduces cost-intensive modifications in later stages. The goal of this contribution is to make the complexity manageable by presenting a method which couples LFT manufacturing and structural simulations in an automated optimization loop. Herein, the influence of linear-elastic, local anisotropic material properties as well as residual stresses resulting from the compression molding of LFT on the stiffness-optimized design of beaded plates is investigated. Based on the simulation studies in this contribution, it can be summarized that the resulting bead height and flank angle, considering anisotropies and residual stresses, are smaller compared to isotropic modelling. As a conclusion, the strength constraint limits the maximum bead height and the flank angle needs to be additionally chosen as a consequence of the local fibre orientations and residual stresses resulting from manufacturing. Optimized bead cross sections are only valid for a specific system under investigation, as they depend on the defined boundary conditions (load case, initial charge geometry and position, fibre orientations, etc.).

Data-driven generative design (DDGD) methods utilize deep neural networks to create novel designs based on existing data. The structure-aware DDGD method can handle complex geometries and automate the assembly of separate components into systems, showing promise in facilitating creative designs. However, determining the appropriate vectorized design representation (VDR) to evaluate 3D shapes generated from the structure-aware DDGD model remains largely unexplored. To that end, we conducted a comparative analysis of surrogate models’ performance in predicting the engineering performance of 3D shapes using VDRs from two sources: the trained latent space of structure-aware DDGD models encoding structural and geometric information and an embedding method encoding only geometric information. We conducted two case studies: one involving 3D car models focusing on drag coefficients and the other involving 3D aircraft models considering both drag and lift coefficients. Our results demonstrate that using latent vectors as VDRs can significantly deteriorate surrogate models’ predictions. Moreover, increasing the dimensionality of the VDRs in the embedding method may not necessarily improve the prediction, especially when the VDRs contain more information irrelevant to the engineering performance. Therefore, when selecting VDRs for surrogate modeling, the latent vectors obtained from training structure-aware DDGD models must be used with caution, although they are more accessible once training is complete. The underlying physics associated with the engineering performance should be paid attention. This paper provides empirical evidence for the effectiveness of different types of VDRs of structure-aware DDGD for surrogate modeling, thus facilitating the construction of better surrogate models for AI-generated designs.

We investigate how four internet of things (IoT) companies perceive the large quantity of community-generated content as a significant source of innovation. We study the extent to which these companies are willing to align their internal organisation to cope with the external community dynamics and define beneficial modes of collaboration for all involved stakeholders. Four IoT companies adopting open-source hardware principles were selected as case studies. The data collection was based on 18 interviews highlighting both the perspectives of the companies and their corresponding communities and the opinions of key experts in the domain. In our findings, we illustrate the different manifestations of open business models and the companies’ concrete approaches to working with external stakeholders. It is shown that companies with a business history more clearly claim sovereignty over their strategic decisions in a community-infused model, while, on the other hand, the community-based companies pursue a community-led strategy.

Design structure matrices (DSMs) are widely known for their ability to support engineers in the management of dependencies across product and organisational architectures. Recent work in the field has exploited product lifecycle management systems to generate DSMs via the co-occurrence of edits to engineering files. These are referred to as dynamic DSMs and results have demonstrated both the efficacy and accuracy of dynamic DSMs in representing engineering work and emergent product architectures. The wide-ranging applicability of the theoretical model and associated analytical process to generate dynamic DSMs enables investigations into the evolving structures within digital engineering work. This paper uses this new capability and presents the results of the world’s first comparison of dynamic DSMs from a set of near-identical systems design projects. Through comparison of the dynamic DSMs’ end-of-project state, change propagation characteristics and evolutionary behaviour, 10 emergent structures are elicited. These emergent structures are considered in the context of team performance and design intent in order to explain and code the identified structures. The significance of these structures for the management of future systems design projects in terms of productivity and efficacy is also described.

Robust designs protect system utility in the presence of uncertainty in technical and operational outcomes. Systems-of-systems, which lack centralized managerial control, are vulnerable to strategic uncertainty from coordination failures between partially or completely independent system actors. This work assesses the suitability of a game-theoretic equilibrium selection criterion to measure system robustness to strategic uncertainty and investigates the effect of strategically robust designs on collaborative behavior. The work models interactions between agents in a thematic representation of a mobile computing technology transition using an evolutionary game theory framework. Strategic robustness and collaborative solutions are assessed over a range of conditions by varying agent payoffs. Models are constructed on small world, preferential attachment and random graph topologies and executed in batch simulations. Results demonstrate that systems designed to reduce the impacts of coordination failure stemming from strategic uncertainty also increase the stability of the collaborative strategy by increasing the probability of collaboration by partners; a form of robustness by environment shaping that has not been previously investigated in design literature. The work also demonstrates that strategy selection follows the risk dominance equilibrium selection criterion and that changes in robustness to coordination failure can be measured with this criterion.

The importance of inter- and transdisciplinary research for addressing today’s complex challenges has been increasingly recognised. This requires new forms of communication and interaction between researchers from different disciplines and nonacademic stakeholders. Demonstrators constitute a crucial communication tool in technology research and development and have the potential to leverage communication between different bodies of knowledge. However, there is little knowledge on how to design demonstrators. This research aims to understand how demonstrators from the fields Internet of Things and Robotics are designed to communicate technology. The goal is to increase the efficiency and effectiveness of demonstrator practice with readily implemented design knowledge and to advance theoretical knowledge in the field of communicating artefacts. We thematically analysed 28 demonstrator design cases, which led to a typology that assists in categorising and understanding 13 key design principles. The typology is built from three perspectives: First, in terms of the overall goal communication, second, in terms of visitor engagement goals (attraction, initial engagement, deep engagement) and third, in terms of resource-related goals (low effort in development and operation). With this typology, we have taken a significant step towards understanding demonstrator design principles for effective technology communication between different stakeholders.

A network modelling approach to educational mapping leads to a scalable computational model that supports adaptive learning, intelligent tutors, intelligent teaching assistants, and data-driven continuous improvement. Current educational mapping processes are generally applied at a level of resolution that is too coarse to support adaptive learning and learning analytics systems at scale. This paper proposes a network modelling approach to structure extremely fine-grained statements of learning ability called Micro-outcomes, and a method to design sensors for inferring a learner’s knowledge state. These sensors take the form of high-resolution assessments and trackers that collect digital analytics. The sensors are linked to Micro-outcomes as part of the network model, enabling inference and pathway analysis. One example demonstrates the modelling approach applied to two community college subjects in College Algebra and Introductory Accounting. Application examples showcase how this modelling approach provides the design foundation for an intelligent tutoring system and intelligent teaching assistant system deployed at Arapahoe Community College and Quinsigamond Community College. A second example demonstrates the modelling approach deployed in an undergraduate aerospace engineering subject at the Massachusetts Institute of Technology to support course planning and teaching improvement.

In recent years, co-creation and collaboration platforms to create and deliver new products and services have taken a step forward; this has led to the development of a new active involvement of users, who from co-designers have become independent designers, even if not experts. Co-design is dynamic and provides the tools to generate democratic design processes guided by the users themselves. The democratization of design tools is the premise for a new paradigm defined ‘Diffuse Design’ by Manzini (2015). This contribution explores the approaches of open design and open production with particular attention to the field of visual communication and the production of motion design artifacts. After an introduction to the co-design framework, the main open-production visual communication platforms are presented to offer an overview of the topic. Next, the potential of online platforms to enable non-designers to produce animated artifacts is explored by examining student projects in a motion design University course. The most significant outputs of the student experience are then described and critically analyzed. Finally, the conclusions investigate the different perspectives for reading the democratization of tools for creating visual artifacts and lay the foundations for future lines of research.

Generative neural networks (GNNs) have successfully used human-created designs to generate novel 3D models that combine concepts from disparate known solutions, which is an important aspect of design exploration. GNNs automatically learn a parameterization (or latent space) of a design space, as opposed to alternative methods that manually define a parameterization. However, GNNs are typically not evaluated using an explicit notion of physical performance, which is a critical capability needed for design. This work bridges this gap by proposing a method to extract a set of functional designs from the latent space of a point cloud generating GNN, without sacrificing the aforementioned aspects of a GNN that are appealing for design exploration. We introduce a sparsity preserving cost function and initialization strategy for a genetic algorithm (GA) to optimize over the latent space of a point cloud generating autoencoder GNN. We examine two test cases, an example of generating ellipsoid point clouds subject to a simple performance criterion and a more complex example of extracting 3D designs with a low coefficient of drag. Our experiments show that the modified GA results in a diverse set of functionally superior designs while maintaining similarity to human-generated designs in the training data set.