To improve understanding of prototyping practice at the fuzzy front end of the design process, this article presents an analysis of a prototyping dataset captured during the IDEA challenge – a 4-day virtually hosted hackathon – using Pro2booth, a web-based prototype capture tool. The dataset comprised 203 prototypes created by four independent teams working in university labs across Europe supported by interviews carried out with each of the teams after the event. The results of the study provide nine key findings about prototyping at hackathons. These include elucidation of the purposes of prototypes in physical, digital and sketch domains and characterisation of teams’ prototyping practices and strategies. The most successful strategy focused on learning about the problem or solution space, often via physical prototypes rather than following more prescriptive ‘theoretical’ methodologies. Recommendations on prototyping strategies in hackathons or similar scenarios are provided, highlighting the importance of practical strategies that prioritise learning and adaptation. The results of this study raise the broader question to the wider research community of how design research and teaching should balance high-level strategic approaches with more hands-on ‘operational’ prototyping.

This study investigates the relationship between the number and type of prototypes developed in rapid prototyping contexts, a team's performance self-estimations, and final actual performance. Findings suggest a strong correlation between each of these elements, with the converse also found to be true, motivating the introduction of the concept of Design Delusion - a type of cognitive dissonance due to differences between perceived and actual states. The paper suggests that early prototyping helps identify and mitigate design delusion, improving design decisions and preventing technical debt.

Design hackathons offer a unique research opportunity to study time-pressured collaborative design. At the same time, research on design hackathons faces unique methodological challenges, prompting the exploration of new research approaches. This paper proposes a new data-collection framework that leverages a virtual format of hackathon events and enables a deeper insight into hackathon dynamics. The framework applicability is presented through a case study of the IDEA challenge hackathon, in which different intrusive and non-intrusive data collection approaches were used.

Generative design is now a core capability of today's CAD systems enabling broader exploration of the design space. This paper presents a desktop review of 15 tools with the aim of characterising where the tools are used in the design process and to what extent they can be considered creative.The results showed that, despite their increasing capabilities, GD tools are unable to exhibit all phases of creativity and cannot be applied in all stages of the design process. This highlights the criticality of the human in design providing opportunities for GD to augment designer creativity.

This article presents an fNIRS experiment investigating cognitive differences between physical and digital prototyping methods in designers (N=25) engaged in open and constrained design tasks. Initial results suggest that physical prototyping yields increased hemodynamic response (i.e., brain activity) compared to digital design, and that constrained design yields increased hemodynamic response compared to open design, in the prefrontal cortex. Further work will seek to triangulate results by investigating potential correlations to design processes and design outputs.

This study explores the application of image generative AI to support design process by creating inspiration boards. Through an evaluative study, we compare the diversity, quantity, fidelity, and ambiguity of boards generated by image generative AI and traditional methods. The results highlight how generative AI produces a quantity of images, it exhibits limited diversity compared to traditional methods. This suggests a tendency for supporting interpolation rather than extrapolation of ideas, in turn providing insights on best practice and into the optimal stage for its application.

Additive manufacturing (AM) has transformed job shop production and catalysed the growth of Makerspaces, FabLabs, Hackspaces, and Repair Cafés. AM has enabled the handling and manufacturing of a wide variety of components, and its accessibility has enabled more individuals to make. While smaller than their production-scale counterparts, the objectives of minimizing technician overhead, capital expenditure, and job response time remain the same. The typical First-Come First-Serve (FCFS) operating model, while functional, is not necessarily the most efficient and makes responding to a-typical or urgent demand profiles difficult. This article reports a study that investigated how AM machines configured with Minimally Intelligent agents can support production in these environments. An agent-based model that simulated 5, 10, 15, and 20 AM machines operating a 9 am−5 pm pattern and experiencing a diverse non-repeating demand profile was developed. Machines were configured with minimal intelligence – FCFS, First-Response First-Serve (FRFS), Longest Print Time (LPT), Shortest Print Time (SPT), and Random Selection logics – that governed the selection of jobs from the job pool. A full factorial simulation totaling 15,629 configurations was run until convergence to a ranked list of production performance – min Job Time-in-System. Performance changed as much as 200%. Performant configurations featured a variety of logics, while the least performant were dominated by FCFS and LPT. All FCFS (a proxy for today’s operations) was one of the least performant configurations. The results provide an optimal set of logics and performance bands that can be used to justify capital expenditure and AM operations in Makerspaces.

Prototyping is a knowledge generation activity facilitating improved understanding of problem and solution spaces. This knowledge can be generated across a range of dimensions, termed knowledge dimensions (KDs), via a range of methods and media, each with their own inherent properties. This article investigates and characterises the relationships between prototypes and knowledge generated from prototyping activities during the design process, by establishing how different methods and media contribute across KDs. In so doing, it provides insights into prototyping activity, as well as affording a means by which prototyping knowledge generation may be studied in detail. The investigation considers sets of prototypes from eight parallel 16-week design projects, with subsequent investigation of the knowledge contributions that each prototype provides and at what stage of the design process. Results showed statistical significance supporting three inferences: i) teams undertaking the same design brief create similar knowledge profiles; ii) prototyping fidelity impacts KD contribution and iii) KDs align with the different phases of the project. This article demonstrates a means to describe and potentially prescribe knowledge generation activities through prototyping. Correspondingly, the article contends that consideration of KDs offers potential to improve aspects of the design process through better prototyping method selection and sequencing.

3D printing technologies, such as material extrusion (MEX), hold the potential to revolutionise manufacturing by providing individuals without traditional manufacturing capabilities with powerful and affordable resources. However, widespread adoption is impeded by the lack of user-friendly design tools due to the necessity of domain-specific expertise in computer-aided design (CAD) software and the overwhelming level of design freedom afforded by the MEX process. To overcome these barriers and facilitate the democratisation of design (DoD), this article introduces an innovative, generative-based design (GBD) methodology aimed at enabling non-technical users to create functional components independently. The novelty of this methodology lies in its capacity to simplify complex design tasks, making them more accessible to non-designers. The proposed methodology was tested in the design of a load-bearing part, yielding a functional component within two design iterations. A comparative analysis with the conventional CAD-based process revealed that the GBD methodology enables the DoD, reflected in a 68% reduction in design activities and a decrease in design difficulty of 62% in requisite know-how and a 55% in understanding. Through the creation and implementation of this methodology, the article demonstrates a pioneering integration of state-of-the-art techniques of generative design with design repositories enabling effective co-design with non-designers.

Design neurocognition is an emerging research area that can provide insights into the black box of designers’ cognitive processes. However, work to date has focused on neurocognition on its own, without integrating this with other design measures. This paper presents the results of a pilot study which brings together designer neurocognition with design output and assessment of the design process followed in a constrained prototyping activity comparing use of physical and digital Lego. This was achieved via EEG data capture, a TLX survey and measures of design output variance. Differences between physical and digital prototyping methods were found with respect to Task Related Powers of EEG signals and the design process followed with digital prototyping methods found to take longer, require more effort and cause more frustration. No differences were found with regard to design output. Whilst the sample size used (n=12) was small, future studies will use large sample sizes to increase their statistical power and will consider alternative EEG or fNIRS to capture brain activity due to challenges with the headset used in this study.

The breadth of media and approaches used when prototyping are vast, with each holding inherent properties that vary their suitability for a given prototyping activity.

While several have established classifications of types and purposes of prototypes, there is little by way of guidance for designers on how select and strategise prototyping given their activity needs, or how the prototype chosen may influence their process, success or efficiency.

This paper presents nine affordances of prototypes derived from literature, together characterising the properties of prototyping media or approaches that affect their suitability across prototyping activities.

The affordances are illustrated through application to physical and digital classes of prototypes and four real prototype cases, showing descriptive capability, inherent differences between the media, and enabling direct and consistent comparison.

By mapping affordances across many media and approaches, this work enables better method selection to align with activity needs, better description and comparison of media and approaches, and the ability the broadly interrogate and direct future development of prototyping technologies.

The International Design Engineering Annual (IDEA) Challenge is a virtually hosted hackathon for Engineering Design researchers with aims of: i) generating open access datasets; ii) fostering community between researchers; and, iii) applying great design minds to develop solutions to real design problems. This paper presents the 2022 IDEA challenge and elements of the captured dataset with the aim of providing insights into prototyping behaviours at virtually hosted hackathons, comparing it with the 2021 challenge dataset and providing reflections and learnings from two years of running the challenge. The dataset is shown to provide valuable insights into how designers spend their time at hackathon events and how, why and when prototypes are used during their design processes. The dataset also corroborates the findings from the 2021 dataset, demonstrating the complementarity of physical and sketch prototypes. With this paper, we also invite the wider community to contribute to the IDEA Challenge in future years, either as participants or in using the platform to run their own design studies.

Machine Learning (ML) techniques are showing increasing use and value in the engineering sector. Object Detection methods, by which an ML system identifies objects from an image presented to it, have demonstrated promise for search and retrieval and synchronised physical/digital version control, amongst many applications.

However, accuracy of detection often decreases as the number of objects considered by the system increases which, combined with very high training times and computational overhead, makes widespread use infeasible.

This work presents a hierarchical ML workflow that leverages the pre-existing taxonometric structures of engineering components and abundant digital models (CAD) to streamline training and increase accuracy. With a two-layer structure, the approach demonstrates potential to increase accuracy to >90%, with potential time savings of 75% and greatly increased flexibility and expandability.

While further refinement is required to increase robustness of detection and investigate scalability, the approach shows significant promise to increase feasibility of Object Detection techniques in engineering.

Prototyping strategies and technology often focus on reducing the fabrication time and cost between design iterations, however, there is limited knowledge about the specific characteristics of change that prototyping strategies aim to impact. To investigate, and better understand these characteristics, this study curates and systematically analyses a representative dataset of 50 'real-world' prototype samples. The study aims to explore the various elements that constitute a design change and to determine their impact on the scale of volumetric change detected. The results highlight emergent patterns and correlations between study metrics to better understand the reasons for design change and the frequency and scale of changes detected in the sample dataset. Findings reveal that the purpose of a design change is, in certain cases, highly correlated to the scale of change affected, and that some changes are more prevalent in the dataset than others, with an average volumetric difference of 4.2% between sample versions detected. The study provides an initial characterisation of prototype change to guide iterative prototyping processes and improve the efficiency and effectiveness of design iterations.

Classifying shape and form is a core feature of Engineering Design and one that we do this instinctively on a daily basis. Matching similar components to then reduce unique component counts, determining whether a competitors design infringes on copyright and receiving market feedback on product styling are all examples where shape and form comes into play. However, shape and form can be perceived in different ways from purely mathematical (e.g. shape grammars) to wholly subjective (e.g. market feedback) and these perceptions may not entirely agree.

This paper examines the mathematical and human perceptions of shape and form through a study of classifying shapes that have been interpolated between one another, and in doing so, highlights the disparity in perceptions. Following this, the paper demonstrates how the emergent field of Machine Learning can be applied to capture mathematical and human perceptions of shape and form resulting in a means to twin this feedback into product development.

Whilst prior works have characterised the affordances of prototyping methods in terms of generating knowledge about a product or process, the types, or ‘dimensions’ of knowledge towards which they contribute are not fully understood. In this paper we adapt the concept of ‘design domains’ as a method to interpret, and better understand the contributions of different prototyping methods to design knowledge in new product development. We first synthesise a set of ten dimensions for design knowledge from a review of literature in design-related fields. A study was then conducted in which participants from engineering backgrounds completed a Likert-type questionnaire to quantify the perceived contributions to design knowledge of 90 common prototyping methods against each dimension. We statistically analyse results to identify patterns in the knowledge contribution of different methods. Results reveal that methods exhibit significantly different contribution profiles, suggesting different methods to be suited to different knowledge. Thus, this paper indicates potential for new methods, methodology and processes to leverage such characterisations for better selection and sequencing of methods in the prototyping process.

Physical prototyping during early stage design typically represents an iterative process. Commonly, a single prototype will be used throughout the process, with its form being modified as the design evolves. If the form of the prototype is not captured as each iteration occurs understanding how specific design changes impact upon the satisfaction of requirements is challenging, particularly retrospectively.

In this paper two different systems for digitising physical artefacts, structured light scanning (SLS) and photogrammetry (PG), are investigated as means for capturing iterations of physical prototypes. First, a series of test artefacts are presented and procedures for operating each system are developed. Next, artefacts are digitised using both SLS and PG and resulting models are compared against a master model of each artefact. Results indicate that both systems are able to reconstruct the majority of each artefact's geometry within 0.1mm of the master, however, overall SLS demonstrated superior performance, both in terms of completion time and model quality. Additionally, the quality of PG models was far more influenced by the effort and expertise of the user compared to SLS.

The importance of prototyping is unanimous with numerous studies into the media, types, roles and properties of prototypes. However, no recent papers have sought to examine and characterise industry practice and if and how this has changed since the early 2000s.

To address this, a snapshot of industrial prototyping practice with particular attention to the what, when, why, how, and by whom is reported. The study involved five small-medium sized design companies based in the South-West of the UK and validation of the findings by two independent practitioners.

The snapshot revealed that 3D printing and virtual prototyping tools have reached widespread adoption in SMEs,that their design processes are highly agile and iterative and are difficult to fit to any extant design process model.

Rather, the approaches appear to implicitly comprise of three levels of design convergence: macro, meso, and micro, which correspond to finer/more detailed changes.

The results also reveal the frequent transitions between digital and physical media and the need to manage these transitions to ensure the product representations in different media are appropriately up-to-date.

The democratisation of design permits greater stakeholder involvement in what has traditionally been a domain reserved for experts; the design process itself. This is enabled by technological advances in fields such as 3D printing, virtual reality and high-speed peer to peer communication technologies which have fuelled the development of new and innovative design methods. This paper compares and contrasts different approaches to the democratisation of design, and in particular, those that aim to involve wider stakeholders in the design process itself. Three different approaches(design by play, design by generation and crowdsourcing for design) are defined and contextualised within existing design frameworks and their respective suitabilities to democratise different design phases are considered. An exemplar use case of each approach is presented in order to assess how stakeholder engagement is affected by each democratising strategy. The discussion compares and contrasts the approaches with respect to their applicability and utility for different stages of the design process and how the power dynamics of the design process are altered when the different approaches are employed.