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.

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.

The rapid pace of development in Digital Engineering has led to an explosion of ideas and new practice in how it can support Engineering Design and Manufacture. You may have heard of the terms Digital Transformation, Digital Twin, Digital Thread, Digital Tapestry and Digital Footprint amongst many other forms of “Digital X” but how have these come about and how do they come together to provide the landscape of what Digitalisation has to offer?

In this paper, we analyse the emergence, definition, use and co-occurrence of “Digital X” terminology from an academic dataset of 19,627 papers curated from Scopus. The results reveal that these terms are being used without being fully contextualised in terms of a hierarchy or equivalent to effectively articulate the Digital landscape.

Through this analysis, an emerging “Digital X” framework is proposed, with evidence given to support suggested links, and knowledge gaps highlighted for further investigation. Once this framework is complete, a rich lexicon describing the Digital Landscape will pave the way for the future in Digital Engineering.

Combinatorial Design such as configuration design, design optioneering, component selection, and generative design, is common across engineering. Generating solutions for a combinatorial design task often involves the application of classical computing solvers that can either map or navigate design spaces. However, it has been observed that classical computing resource power-law scales with many design space models. This observation suggests classical computing may not be capable of modelling our future design space needs.

To meet future design space modelling needs, this paper examines quantum computing and the characteristics that enables its resources to scale polynomially with design space size. The paper then continues to present a combinatorial design problem that is subsequently represented, constrained and solved by quantum computing. The results of which are the derivation of an initial set of circuits that represent design space constraints. The study shows the game-changing possibilities of quantum computing as an engineering design tool and is the start of an exciting new journey for design research.

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.

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.

SAR provides an unobtrusive implementation of AR and enables multiple stakeholders to observe and interact with an augmented physical model. This is synonymous with co-design activities and hence, there is a potential for SAR to have a significant impact in the way design teams may set-up and run their co-design activities in the future. Whilst there are a growing number of studies which apply SAR to design activities, few studies exist that examine a particular element of a design activity in a controlled manner. This paper will begin to fill this gap through the controlled study of SAR and its effects on the communication between participants of a co-design activity. To do so the paper compares a controlled design session, using more traditional methods of design representations (3D models on a screen), to sessions run using SAR. The sessions are then analysed to gather information on the gestures used by the participants as well as the overall efficiency of the participants at completing the set design task. The paper concludes that the data gathered tentatively supports a link between the use of SAR and improved communication between design session participants.

Dealing with component interactions and dependencies remains a core and fundamental aspect of engineering, where conflicts and constraints are solved on an almost daily basis. Failure to consider these interactions and dependencies can lead to costly overruns, failure to meet requirements, and lengthy redesigns. Thus, the management and monitoring of these dependencies remains a crucial activity in engineering projects and is becoming ever more challenging with the increase in the number of components, component interactions, and component dependencies, in both a structural and a functional sense. For these reasons, tools and methods to support the identification and monitoring of component interactions and dependencies continues to be an active area of research. In particular, design structure matrices (DSMs) have been extensively applied to identify and visualize product and organizational architectures across a number of engineering disciplines. However, the process of generating these DSMs has primarily used surveys, structured interviews, and/or meetings with engineers. As a consequence, there is a high cost associated with engineers' time alongside the requirement to continually update the DSM structure as a product develops. It follows that the proposition of this paper is to investigate whether an automated and continuously evolving DSM can be generated by monitoring the changes in the digital models that represent the product. This includes models that are generated from computer-aided design, finite element analysis, and computational fluid dynamics systems. The paper shows that a DSM generated from the changes in the product models corroborates with the product architecture as defined by the engineers and results from previous DSM studies. In addition, further levels of product architecture dependency were also identified. A particular affordance of automatically generating DSMs is the ability to continually generate DSMs throughout the project. This paper demonstrates the opportunity for project managers to monitor emerging product dependencies alongside changes in modes of working between the engineers. The application of this technique could be used to support existing product life cycle change management solutions, cross-company product development, and small to medium enterprises who do not have a product life cycle management solution.