Mixed Reality enables individuals to visualise and interact with artefacts and environments through a combination of physical and virtual assets. It has received increased interest from the design community as a means to accelerate, enrich and enhance prototyping activities. This article concerns MR’s ability to deceive an individual through the combination of virtual and physical assets and their underlying traits (e.g., mass, size), and a user’s cognitive ability to ‘join the dots’. If properly implemented, MR could save time and resources by reducing the required prototype fidelity and the need to fully realise variants. However, there is a gap in understanding how the traits of physical and virtual assets and cognition combine to form reality. This article presents a study investigated the role mass, virtual and physical model size played on users perception of an MR prototype. The relative impact of these factors was determined by varying these parameters and assessing the user’s perceived change. The key finding from this study was that the virtual model size had a far greater influence on prototype perceived by the user. This suggests that the required physical fidelity of an MR prototype can be lower than the virtual. Furthermore, exploring size design variants can be achieved exclusively through changes to the virtual model.

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.

Digital Twins are perceived differently between and within industry and academia regarding applications and potentials. For this reason, a round table was formed based on the Digital Twin Workshop of the Design Conference 2022. One of the results of this round table is this contribution, which deals with a survey within the industry. The survey captured the understanding of the different roles in the creation and use of Digital Twins, the requirements and hurdles as well as the perception of methodological support. In addition, factors that influence the perception were identified.

In Fused Filament Fabrication, there is increasing interest in the potential of composite filaments for producing complex and load-bearing components. Carbon fibre-filled polyamide currently has highest available strength and stiffness, but promising variants are not in filament form. This paper investigates filament production of commercially available, high-filled PA-CF pellets by modifying a tabletop filament extruder. We show filament production is possible by improving cooling. The FFF printed specimens show an average UTS of 135.5 MPa, higher than most commercially available filaments.

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.

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.

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.

Laboratory-based experiments show ageing negatively impacts navigation abilities, yet a paucity of research explores lived experience. This exploratory study examined young-old adults' experiences of declining navigation abilities during 16 semi-structured telephone interviews. Findings reveal: (a) ‘Behavioural drivers’ that underpinned the participants' experiences and actions when engaging with their environments, (b) ‘Avoidance’ and (c) ‘Active’ strategies that were adopted by the participants. Declining cognitive function appeared to have a negative impact on participants' perceived abilities and confidence to navigate unfamiliar outdoor environments, which in turn influenced the strategies they chose to adopt. Future psychosocial interventions should draw on neuropsychological theory to ensure retention of navigation skills and confidence for as long as possible.

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.

Product prototypes and particularly those that are 3D printed will have mass properties that are significantly different from the product they represent. This affects both functional performance and stakeholder perception of the prototype. Within this work, computational emulation of mass properties for a primitive object (a cube) is considered, developing a baseline numerical method and parameter set with the aim of demonstrating the means of improving feel in 3D printed prototypes. The method is then applied and tuned for three case study products – a games controller, a hand drill and a laser pointer – demonstrating that product mass properties could be numerically emulated to within ~1% of the target values. This was achieved using typical material extrusion technology with no physical or process modification. It was observed that emulation accuracy is dependent on the relative offset of the centre of mass from the geometric centre. A sensitivity analysis is further undertaken to demonstrate that product-specific parameters can be beneficial. With tuning of these values, and with some neglect of practical limitations, emulation accuracy as high as ~99.8% can be achieved. This was shown to be a reduction in error of up to 99.6% relative to a conventional fabrication.

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.

3D printing is a widely used technology for automating the fabrication of prototypes. The benefits are wide reaching, and include low required expertise, accurate geometric form and the processibility of many materials. However, production of certain forms – especially large forms – can be slow. From review of the sub-systems, the hotend is commonly found to be the limiting factor. To improve this, a modified nozzle design is considered that incorporates a flat copper plate within the flow stream. Analytical simulation was used to guide this design before experimental methods validated the modifications. The maximum volumetric rate for the standard hotend nozzle is 14 mm3/s. The best performing modified nozzle increased the maximum volumetric flow rate to 26 mm3/s – an 86% increase. A series of popular parts were further considered, demonstrating a maximum ∼48% fabrication time reduction, and a mean of ∼23%. This enables 3D printed prototypes to be made more efficiently – both with regards to the design cycle and energy use – and allows designers to use the technology more rapidly than previously possible. By extension, this improves the efficiency of the design process.

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.

Simulation is fundamental to many engineering design processes and powers the field of computational design. Simulation inherently consumes energy resulting in CO2 emissions that impact our environment. While one can source energy from renewable sources and use energy efficient hardware, efforts need to also be made in how we can use simulation in a sustainable manner.

This paper presents a sustainable simulation framework that borrows concepts from web services. The framework makes it easy for engineering firms to adopt and embed sustainable simulation practices thereby removing the burden from the designer tin thinking about how to design sustainably. An illustrative example reveals a 25% reduction in computational effort can be achieved by adopting the framework.

Advancements in prototyping technologies – haptics and extended reality – are creating exciting new environments to enhance stakeholder and user interaction with design concepts. These interactions can now occur earlier in the design process, transforming feedback mechanisms resulting in greater and faster iterations. This is essential for bringing right-first-time products to market as quickly as possible.

While existing feedback tools, such as speak-aloud, surveys and/or questionnaires, are a useful means for capturing user feedback and reflections on interactions, there is a desire to explicitly map user feedback to their physical prototype interaction. Over the past decade, several hand-tracking tools have been developed that can, in principle, capture product user interaction.

In this paper, we explore the capability of the LeapMotion Controller, MediaPipe and Manus Prime X Haptic gloves to capture user interaction with prototypes. A broad perspective of capability is adopted, including accuracy as well as the practical aspects of knowledge, skills, and ease of use. In this study, challenges in accuracy, occlusion and data processing were elicited in the capture and translation of user interaction into design insights.

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.