Automating modelling activities in computer-aided design (CAD) systems is no exception within design automation, one of the current research endeavours aiming to use and transform design-related data in design decision-making processes and the generation and evaluation facilitation of new design solutions. The paper explores the differences between CAD models based on their feature-based CAD modelling sequences that lead to the final models' design. The dataset collected and structured for the study contains more than 1400 CAD models clustered on two levels by using an unsupervised K-means clustering algorithm. The algorithm is performed on the number (total and unique) and the first-order Markov model transition matrices of the CAD modelling operations and their sequential order, respectively. Therefore, three and ten groups (clusters) of CAD models are obtained regarding the level of clustering. The results show that most of the obtained groups are specified by the dominant transition between particular modelling operations. In addition, the study also provides insight into the potential of using feature-based CAD modelling operations' sequences as a first step toward automating the user interaction with the CAD system.

Additive manufacturing has the potential to decrease the climate impact of aviation by providing more light-weight designs. Sustainability is however required to be assessed from a systemic view, including all lifecycle phases, and from a social, ecologic, and economic dimension. This is however challenging in early phase design, where also a large design space need to be explored. A case study is carried out with an aerospace company where two candidate engineering design tools are combined to address this. The integration of these two engineering tools are applied on a Turbine Rear Structure, and shows promising results in enabling a systemic view of sustainability to be integrated and assessed in early phase design space explorations of additive manufactured components. It is recommended that the integration between the two tools is further established and validated.

Citation is a key metric in academia, and it can help to understand how ideas travel between disciplines. In this article, we report on a citation analysis of forty-four articles identified during a systematic literature review of healthcare design research published in six leading design journals. Using the Web of Science's categorisation of journals, we analyse which disciplines cite these forty-four articles. We find that these articles are much more cited in technology and engineering journals than in health sciences. We discuss these findings and the limitations of the study.

To design a more robust artifact, an artifact design based on a design rationale analysis is pivotal. Errors in previous design rationales that led to the degradation of artifact robustness in the past provide valuable knowledge towards improving the robust design. However, methods for exposing and analysing errors in design rationale remain unclear. This paper proposes a structured method for a design rationale analysis based on logical structuring. This method provides a well-constructed means of identifying and analysing errors in design rationale from the perspective of knowledge operation.

In this paper we use formal tools from category theory to develop a foundation for creating and managing models in systems where knowledge is distributed across multiple representations and formats. We define a class of models which incorporate three different representations---computations, logical semantics, and data--as well as model mappings (functors) to establish relationships between them. We prove that our models support model merge operations called colimits and use these to define a methodology for model integration.

The modular lightweight design attempts to reconcile the partially conflicting goals between modularization and lightweight design in order to establish a harmonized modular hybrid design. This requires a close exchange of the resulting development data between the two areas. In this contribution a concept for an interface for the data exchange between system models and FEM models is presented and successfully implemented in the Cameo Systems Modeler and applied to examples from the aircraft cabin. With the interface the homogenization step of modular lightweight design can be performed.

Flexibility analysis helps improve the expected value of engineering systems under uncertainty (economic and/or social). Designing for flexibility, however, can be challenging as a large number of design variables, parameters, uncertainty drivers, decision making possibilities and metrics must be considered. Many available techniques either rely on assumptions that are not suitable for an engineering setting, or may be limited due to computational intractability. This paper makes the case for an increased integration of Machine Learning into flexibility and real options analysis in engineering systems design to complement existing design methods. Several synergies are found and discussed critically between the fields in order to explore better solutions that may exist by analyzing the data, which may not be intuitive to domain experts. Reinforcement Learning is particularly promising as a result of the theoretical common grounds with latest methodological developments e.g. decision-rule based real options analysis. Relevance to the field of computational creativity is examined, and potential avenues for further research are identified. The proposed concepts are illustrated through the design of an example infrastructure system.

This paper addresses the problem of finding a robust optimal design when uncertain parameters in the form of crisp or interval sets are present in the optimization. Furthermore, in order to make the approach as general as possible, direct search methods with the help of sensitivity analysis techniques are employed to optimize the design. Consequently, the presented approach is suitable for black box models for which no, or very little, information of the equations governing the model is available. The design of an electric drivetrain is used to illustrate the benefits of the proposed method.

The paper concerns the Finnish product development teacherś perceptions on their pedagogical content knowledge in higher education settings. The aim is to describe and analyse what kind of pedagogical content knowledge the teachers have and, therefore, to provide a better understanding of the type of knowledge unique to product development teaching. The model of pedagogical content knowledge used here includes the components of product development content knowledge, pedagogical knowledge and pedagogical content knowledge. Based on seven teacher interviews, the main content knowledge concerns the process of product development, its different phases and methods as well as the usage of different software programs. The teachers use diverse teaching methods and their attitude towards educational technology is mostly positive. Course learning outcomes and working life are acknowledged when planning teaching, but only a few teachers take curriculum into account and participate in curriculum design. Even though the teachers use different evaluation methods in teaching, new ways of evaluation are needed. This may be something that innovative educational technology tools can make possible.

Design representations are important tools for designers in the design process. To help designers choose the appropriate representation, taxonomies have been proposed based on type, degree of fidelity, and when to use them. However, Design representations may also play an important role in designers' communication with users and enabling users as co-designers. Therefore, new taxonomies, focusing on design representations’ potential mediating roles in collaborative design processes with users, have been developed. The purpose of this interview study, with twelve designers within Swedish industry, was thus to investigate how designers use design representations in communication with users in the design process. The study indicates that the designers mainly interacted with users in order for them to answer specific design questions or to evaluate design solutions. If design representations’ value for facilitating communication and collaboration with users should be emphasized, we need to shift from teaching mainly taxonomies related to fidelity levels or when to use them in the design process, and rather educate future designers about design representations inherent potential to mediate and enhance the dialogue with users.

This paper proposes that safety footwear manufacture shifts towards a sustainable Product-Service System (PSS). The PSS consists of seven stages from identification of requirements through to disposal or replacement. Through this, designers and manufacturers can realise safety footwear that is customised to end user sizing, function, industry and aesthetic requirements. For end users, this PSS leads to the creation of more comfortable and practicable footwear, addressing the underlying level of acceptance of uncomfortable safety footwear, reducing waste and providing sex specific offerings.

Using the case study of a smart green blue roof pilot project in Greater Manchester, the authors have coded and analysed a series of semi structured interviews. We present our findings which are specifically focused on how 'smart' was understood by the multiple stakeholders involved in the project and how product complexity is managed when digital technologies are integrated into nature based solutions that are becoming more popularised in the construction sector. This integration of digital twin and sensor technologies with physical drainage products to create the next generation of smart green blue infrastructure, presents numerous challenges for organisations in the construction sector. We conclude that the need for smart systems of systems in this sector necessitates organisational change and new methods of knowledge transfer across organisations who work together to deliver the holistic physical and digital services to the client.

Numerous visualisation tools based on digital and cross-reality (XR) platforms are emerging in industrial design. They offer new possibilities and promising advantages to the designers but also raise challenges for design education. This study investigates the learning experience of emerging tools in education and its influences on tool-use behaviours and attitudes in design practice. Digital sketching is selected for the case study as the emerging tool compared to more established tools (e.g., traditional sketching and CAD). Semi-structured interviews were conducted with 12 practising designers to compare their learning experience of digital sketching versus traditional sketching and CAD modelling in formal education. Results indicate that designers’ learning experience in education can have an influence on not only their skill sets but also attitudes towards engaging with emerging visualisation tools as they progress in their careers. The study suggests that embracing emerging design visualisation tools in education now is essential for helping our future designers be more resilient and flexible with tool selection and use in their future design practice.

Engineering knowledge forms an essential part of our planetary fight against climate change. Traditionally, engineering curricula emphasizes the importance of technical knowledge and encourages to specialize in niche areas, where engineers develop themselves into experts. However, it is important to be able to reflect on complex societal challenges from a variety of perspectives to produce not only innovative, but also long-lasting and inclusive solutions for the greater good. This paper aims to understand the extent of systems thinking abilities of engineers by differentiating experts from novices. The study traces sustainability connections made by professional engineers and master's level engineering students when solving engineering design problems. This qualitative study highlights seven recurring themes that relate to the global sustainability discourse and describe a problem-centred approach through a real-life case that focuses on paper and pulp production, through a thematic analysis of 59 responses. The results portray system-level differences in how novice and expert engineers approach sustainability questions and how these differences shape their solution spaces.

The design of various products is driven by requirements that describe extremes. In marine structural design, joint extremes of environmental variables like wave height and wind speed are used to define load cases. Similarly, in ergonomic design minimum and maximum values of anthropometric variables are considered to make sure a product is suitable for a wide range of users. Here, we present a method that supports designers to define requirements using joint extreme values: the requirements contour method. The method is based on structural engineering's environmental contour method and uses a dataset and statistical methods to specify a region in the variable space that must be considered in the design process. That region's enclosure is the requirements contour and holds the joint extremes. After formally describing the method, we give an illustrative example of its usage: we use it to define requirements for the design of an ergonomic handle for a power tool. The requirements contour method is a field-independent approach to design for extremes. In the tradition of design for X, we think that a design project can benefit from applying methods that focus on different 'X's.

Digitalization is one of the fundamental pillars of Industry 4.0. Within smart factories, Big Data Analytics systems play a key role in supporting the decision-making process of various stages of business processes. In this context, this research aims to identify solutions able to process large volumes of data from digital business processes with the final goal of adding value to the organisation. More specifically, the research deals with the implementation of a digital manufacturing tool able to digitize the workforce management process. The research has been applied in the case study of an Italian manufacturing company operating in the leather goods sector through the digitalization of the workforce management by a cloud-based platform. The implementation of the tool increases the efficiency of the production process, provides efficient management and integrates workforce data into one system. The implemented tool generates a large volume of data, the final goal is to make data user-friendly to support business decisions. Digitisation provides an exchange of information to support managers to make confident decisions.

For the past 5 years, more and more established technological firms have structured domain of expertise dedicated to breakthrough innovation management, in order to foster their innovative capabilities. Our paper studies how such expertise may be organised to identify and exploit more effectively new design paths. Our case study is based on the Renault's experts who demonstrate how the design theories could support the firm's innovation capabilities through an exploration partnership. The conclusion presents new co-exploration models and proposes organisations of the expert's activities.

Mixed Reality (MR) technologies are widely available and applied in a variety of design and engineering applications. MR prototypes capture the respective benefits of physical and digital prototypes by merging these domains saving the time and resources required to create them. This advantage is compelling in the context of design education where tight time and resource constraints exist. However, it is known that new digital prototyping tools can cause problems for students applying appropriate prototyping tools during practice-based studio design projects. Our paper contributes a systematic appraisal of MR prototyping's proposed dimensions value against constraints and issues in design studio education. This highlights MR Visualisation and Knowledge Management dimensions as most readily realised in education. Recommendations are then reflected on via an illustrative case study into the implementation of MR prototyping via these dimensions. Reflections corroborate the value proposition, but also highlight a need for further research exploring activities to scaffold MR prototyping to further support reflective design thinking.

In Additive Manufacturing (AM), new generations of polymer composites presented as engineering- grade materials provide high-end mechanical properties with the design freedom AM provides. Interlayer anisotropy is the main challenge in both in-situ optimization and post-analysis in transitioning from prototypes to high-performance components in fused filament fabrication (FFF). Recent studies show a direct correlation between layer fusion temperature and mechanical properties. In this paper, we present synchronized position and temperature data and study how a component changes based on layer height and geometry. An IR sensor transfers data while printing a G-code generated by FullControllGcode, printing in a single direction and recording temperature in front of the nozzle. Results show that within each layer, a Δt of 20°C at thinner geometries, the heat loss will provide a reduction in mechanical properties and further heat loss occurs when moving away from the heated bed. By using the presented temperature mesh in further studies, post- printed anisotropic components can be analyzed by FEM, and the FFF process can be adaptively optimized based on location, size and geometry.

use-case scenarios, including homes, hospitals, workplaces, and recreation. Though the area of Social Robotics has gained traction in recent years, the majority of the studies so far have studied single-human and single-robot interaction. In comparison, Social Robots are increasingly being placed in human teams, likely affecting team dynamics. On the other hand, Engineering teams work together to deliver outstanding results and the processes in these teams are social. We propose that Social robot can be added to engineering human team to enhance team cohesion and performance. Therefore, this paper presents a preliminary framework towards developing a conceptual framework to study team cohesion in Human-Robot Teams (HRTs) in engineering context, looks at different roles of social robot and how the responses, behaviours, emotions of social robots shape outcomes in the engineering team. The research specifically focuses on team cohesion because team cohesion is reportedly one of the most critical concepts in team dynamics. The paper outlines the research objectives, framework and concept workflow.