The Research Journey Map is introduced to guide researchers on creating engaging, meaningful and impactful presentations and publications. Built on the foundational work of the Hero's Journey by Joseph Campbell, this template helps technical researchers communicate information, data, systems and artifacts that result from research so that audiences grasp and embrace the research findings.

Applications of machine learning technologies are becoming ubiquitous in many sectors and their impacts, both positive and negative, are widely reported. As a result, there is substantial interest from the engineering community to integrate machine learning technologies into design workflows with a view to improving the performance of the product development process. In essence, machine learning technologies are thought to have the potential to underpin future generations of data-enabled engineering design system that will deliver radical improvements to product development and so organisational performance. In this paper we report learning from experiments where we applied machine learning to two shape-based design challenges: in a given collection of designed shapes, clustering (i) visually similar shapes and (ii) shapes that are likely to be manufactured using the same primary process. Both challenges were identified with our industry partners and are embodied in a design case study. We report early results and conclude with issues for design descriptions that need to be addressed if the full potential of machine learning is to be realised in engineering design.

The emergence of the Internet of Things (IoT) has brought more challenges for designers to fully understand networked objects and develop pleasurable user experiences (UXs). Due to the radical change of products when they are connected, traditional experience design theories may not be applicable in this new context. Based on two well-established UX design theories, this paper presents a survey study that investigated the pleasurability of IoT devices by comparing a representative IoT device (i.e., the smartwatch) and its conventional form (i.e., the wristwatch). An online questionnaire was deployed to gather feedback from parallel wristwatch and smartwatch users. Their experiences using both types of watches were quantitatively and qualitatively compared by data analysis. The results highlighted the differences in UXs between smartwatches and wristwatches in three types of pleasure and five psychological needs. The study revealed design opportunities to improve the pleasurability of smartwatches and provides novel design insights informing the development of pleasurable UXs for future IoT devices.

The need to understand users and products is one of the cores for product success. There are many ways to reach this understanding,such as,interviews and consumer cocreation. This study put a focus on the application of product usage information (PUI). Although amount of product-related information is available in the middle of a product's life, producers have not realized the full potential of these data in product development. The academic literature describes various use cases that outline how PUI supports this kind of understanding, and eventually benefits product development. Each of them provides fragmented information from certain perspectives. This diversity and the lack of systematic overview facilitates a fragmentation in the research of PUI usage in product development. To have an initial and unified overview of PUI's value on the understanding of users and products, this paper conducted a combination of systematic and descriptive review to form a sample with 12 papers. The result indicates that PUI increases producer's understanding about the product, its users and the context of usage. However, producers need to address several challenges if they want to apply PUI in product development successfully.

Generative design (GD) algorithms is a fast growing field. From the point of view of Design Science, this fast growth leads to wonder what exactly is 'generated' by GD algorithms and how? In the last decades, advances in design theory enabled to establish conditions and operators that characterize design generativity. Thus, it is now possible to study GD algorithms with the lenses of Design Science in order to reach a deeper and unified understanding of their generative techniques, their differences and, if possible, find new paths for improving their generativity.

In this paper, first, we rely on C-K ttheory to build a canonical model of GD, based independent of the field of application of the algorithm. This model shows that GD is generative if and only if it builds, not one single artefact, but a “topology of artefacts” that allows for design constructability, covering strategies, and functional comparability of designs. Second, we use the canonical model to compare four well documented and most advanced types of GD algorithms. From these cases, it appears that generating a topology enables the analyses of interdependences and the design of resilience.

Engineers often need to discover and learn designs from unfamiliar domains for inspiration or other particular uses. However, the complexity of the technical design descriptions and the unfamiliarity to the domain make it hard for engineers to comprehend the function, behavior, and structure of a design. To help engineers quickly understand a complex technical design description new to them, one approach is to represent it as a network graph of the design-related entities and their relations as an abstract summary of the design. While graph or network visualizations are widely adopted in the engineering design literature, the challenge remains in retrieving the design entities and deriving their relations. In this paper, we propose a network mapping method that is powered by Technology Semantic Network (TechNet). Through a case study, we showcase how TechNet’s unique characteristic of being trained on a large technology-related data source advantages itself over common-sense knowledge bases, such as WordNet and ConceptNet, for design knowledge representation.

Patent analysis is a popular topic of research. However, designers do not engage with patents in the early design stage, as patents are time-consuming to read and understand due to their intricate structure and the legal terminologies used. Manually produced graphical representations of patent working principles for improving designers’ awareness of prior art have been demonstrated in previous research. In this paper, an automated approach is presented, utilising Natural Language Processing (NLP) techniques to identify the invention working principle from the patent independent claims and produce a visualisation. The outcomes of this automated approach are compared with previous manually produced examples. The results indicate over 40% match between the automatic and manual approach, which is a good basis for further development. The comparison suggests that the automated approach works well for features and relationships that are expressed explicitly and consistently but begin to lose accuracy when applied to complex sentences. The comparison also suggests that the accuracy of the proposed automated approach can be improved by using a trained part-of-speech (POS) tagger, improved parsing grammar and an ontology.

There have been growing uses of semantic networks in the past decade, such as leveraging large-scale pre-trained graph knowledge databases for various natural language processing (NLP) tasks in engineering design research. Therefore, the paper provides a survey of the research that has employed semantic networks in the engineering design research community. The survey reveals that engineering design researchers have primarily relied on WordNet, ConceptNet, and other common-sense semantic network databases trained on non-engineering data sources to develop methods or tools for engineering design. Meanwhile, there are emerging efforts to mine large scale technical publication and patent databases to construct engineering-contextualized semantic network databases, e.g., B-Link and TechNet, to support NLP in engineering design. On this basis, we recommend future research directions for the construction and applications of engineering-related semantic networks in engineering design research and practice.

The success of engineering product development depends on the effective communication of design descriptions in formats that suit the needs and capabilities of all stakeholders involved in the delivery to market and through-life support of products. Configuration management is a core design process to ensure the consistency of the technical data package, i.e., the collection of design descriptions needed to support the development, manufacture and operation of a given product. Bills of Materials (BoMs) are critical parts of the technical data package because they act as integrators: adapting detailed design descriptions to suit the needs of particular downstream processes. The ability to reconfigure BoMs while maintaining internal consistency of the technical data package (where all BoM configurations are complete and compatible with each other) is a major challenge. In this paper, we introduce research exploring computational tools that could support engineers in manipulating BoMs while also maintaining the internal consistency of the technical data package.

Design & Manufacture Knowledge Mapping is a critical activity in medium-to-large organisations supporting many organisational activities. However, techniques for effective mapping of knowledge often employ interviews, consultations and appraisals. Although invaluable in providing expert insight, the application of such methods is inherently intrusive and resource intensive. This paper presents word co-occurrence graphs as a means to automatically generate knowledge maps from technical documents and validates against expert generated knowledge maps.

The capability to manufacture at home is continually increasing with technologies, such as 3D printing. However, the ability to design products suitable for manufacture and use remains a highly-skilled and knowledge intensive activity. This has led to ‘content creators’ providing vast repositories of manufacturable products for society, however challenges remain in the search & retrieval of models. This paper presents the surrogate model convolutional neural networks approach to search and retrieve CAD models by mapping them directly to their real-world photographed counterparts.

ABSTRACT

While extensive modelling - both physical and virtual - is imperative to develop right-first-time products, the parallel use of virtual and physical models gives rise to two interrelated issues: the lack of revision control for physical prototypes; and the need for designers to manually inspect, measure, and interpret modifications to either virtual or physical models, for subsequent update of the other. The Digital Twin paradigm addresses similar problems later in the product life-cycle, and while these digital twins, or the “twinning” process, have shown significant value, there is little work to date on their implementation in the earlier design stages. With large prospective benefits in increased product understanding, performance, and reduced design cycle time and cost, this paper explores the concept of using the Digital Twin in early design, including an introduction to digital twinning, examination of opportunities for and challenges of their implementation, a presentation of the structure of Early Stage Twins, and evaluation via two implementation cases.

There are three product design contexts that may significantly affect the design of a product and customer preferences towards product attributes, i.e. customer context, market context, and usage context factors. The conventional methods to gather product usage contexts may be costly and time consuming to conduct. As an alternative, this paper aims to automatically identify product usage contexts from publicly available online customer reviews. The proposed methodology consists of Preprocessing, Word Embedding, and Usage Context Clustering stages. The methodology is applied to identify usage contexts from laptop customer reviews, which results in 16 clusters of usage contexts. Furthermore, analyzing the review sentences explains the separation of “playing games” –which is more related to casual gaming, and “gaming rig” –which implies high computing power requirements. Finally, comparing customer review with manufacturer's product description may reveal a discrepancy to be investigated further by product designer, e.g. a customer suggests a laptop for basic use, although the manufacturer's description describes it for heavy use.

In recent years, product complexity in terms of function and structure has been driven by technological development in complementary components. Designing unbiased product evaluation metrics being to grasp the complex relationships of product features, and able to capitalize on market needs has become a challenge in industrial practice.

In this paper, we propose a hybrid framework in which evaluation models are generated by integrating Interpretive Structural Modeling (ISM), Hierarchical Clustering and Data Envelopment Analysis (DEA). Whereas ISM constructs hierarchical digraphs (skeletons), Hierarchical Clustering reduces dimensionality of pairwise comparisons (correlations) of design variables, and suggests possible evaluation configurations, and DEA computes weights to provide optimal evaluation metrics. Our computational experiments using more than twenty thousand vehicles from 1982 to 2013 confirmed the feasibility and usefulness of DEA with hierarchical concepts to generate the optimal vehicle evaluation metric, and to suggest configurations for vehicle design layouts.

The observation of designers' behaviour in collaborative design activities and the analysis of protocols improved the understanding of how novel ideas emerge, what occurs among designers and, indirectly, what methods have a good impact on the outcomes. Yet, protocol analysis requires recording the design sessions, often in a simulated environment, thus introducing a bias in the observation. Moreover, the analysis takes up to 1000 times the duration of the observed design session. These limitations definitely hinder the scalability of this practice to large experiments in real operational environments.

This paper investigates the possibility to use the data collected in log files, automatically recorded during collaborative design sessions assisted by an ICT design support tool, as a means to extract relevant information about the design process and ultimately to infer insights about co-designers' cognition during the session. In this perspective, the paper proposes a set of metrics tailored to an Augmented Reality-based collaborative design tool. The study has been carried about by processing the data collected in 5 real case studies conducted in three different design companies.