Digital fabrication laboratories play a role as an educational environment in which different learning activities incorporate advanced technological developments. Digital fabrication design education often involves exploratory and scaffolded processes of materialising ideas into products. However, FabLabs poses multiple challenges for pedagogy and design learning. Based on a large-scale digital fabrication course in a higher education institution, we examine whether teamwork carried out in a digital fabrication environment improves creativity. Furthermore, we analyse if teamwork affects self-assessment of learning activities involving building tangible artefacts. Finally, we examine whether the type of produced prototype affects the team's overall performance. The results allow for digital fabrication design education recommendations, including interventions intended for improving the creativity of the outcomes, team performance, and learning of different digital fabrication issues.

The design rationale describes the justification of design decision or selection. To avoid unnecessary design iterations, a capturing and documentation of this rationale is highly desirable. In digital engineering processes it is of imminent importance not only to document the evaluation processes behind this rationale but to make them repeatable and digitally executable. This allows to design a variety of product variants within an engineering framework. This paper explains an approach based on graph-based design languages and presents it based on a section of an automotive assembly system.

It is important to be able to compare and evaluate different solutions early in development. This paper proposes a method for structuring historical data into a data model that can support the evaluation of new design concepts. The data is contextualized by linking it to a hierarchical decomposition of existing products. Two case studies were conducted to evaluate the value of using historical data when evaluating new concepts. The cases confirm that the proposed method is useful for evaluation of new concepts.

With the increasing implementation of artificial intelligence (AI) in the design process, it is crucial to understand how users will accept AI-designed products. This work studies how the public currently perceives an AI's design capability as compared to a human designer's capability by conducting an online survey of 205 people via Amazon Mechanical Turk. The survey collects the respondents' perception on 16 specific bicycle design goals, demographic information, and self-reported level of design and AI/ML knowledge. Findings reveal that people think an AI would perform worse than a human designer on most design goals, particularly the goals that are user-dependent. This work also shows that the higher people's self-reported level of knowledge in design and the older they are, the more likely they are to think an AI's design capability would exceed a human designer's capability. The insights from this work add to the understanding of user acceptance of AI-designed products, as well as human designers' acceptance of AI input in human-AI teams.

The digitalisation of the industry offers new opportunities to discuss design activities and support tools. Advancement in AI allows thinking about new Designer-AI tools interaction in the design process. The paper aims to initiate a characterisation of tools issued from researches in the application of AI in Design to rethink the division of work between Designer-AI tools. The paper is based on the literature on the concept of Levels of Automation in cognitive engineering, manufacturing and robotics, and proposes a grid of characterisation of the Level of Automation for the design process.

The use of exoskeletons promises improved ergonomics, empowerment of users and prevention of musculoskeletal disorders. However, the development process is complex and a generic development methodology that will guide and assist designers through it is missing. The goal of this paper is to describe a methodological approach that will assist the conceptual design of exoskeletons. Based on derived methodological requirements, activities 1, 2, and 3 of the VDI 2221 (Methodology for the development of technical products) are specified to adapt the generic guideline to the development process of exoskeletons. These activities include the analysis and determination of the relationship between the use case, product requirements and motions, technical functions, and design solutions. For generating a list of product requirements designers must focus on the workers’ motions and needs for a for a task-specific and personalised development. Use case specific movements are generalised by using rotational and translational basic movements that result in six degrees of freedom and from which a function structure is derived. The method is critically reviewed based on the established methodological requirements.

Machine learning (ML) is a well-established research topic in Industry 4.0 is boosting its adoption. ML is also used for manufacturing cost estimation during design. Such approaches are commonly used to estimate the cost of mass-produced parts. Many consolidated historical data are available for training the regression models. Unfortunately, very often, such a database of data is not available.

The paper defines an ML approach for parametric cost estimation of axisymmetric components. The data for training the ML model derives from automatic software for analytically estimating the manufacturing cost. With a proper set of simulations, the tool can generate a large amount of data for training. The paper presents the steps for developing a parametric cost model using ML. The approach is based on CRoss Industry Standard Process for Data Mining method. The proposed method was used to develop one cost model (to estimate the total cost that considered raw material and manufacturing cost). The obtained Relative Error is 23.52% ± 1.37%, coherent with E2516 − 11, Standard Classification for Cost Estimate Classification System.

Prototyping uses many models over its process, with varying strength and weakness offered by those in the physical vs digital domains. Working across domains is necessary but introduces a transition cost, process complexities, and risks lost learning. Drawing from Digital Twinning, this work explores the creation of integrated physical-digital prototyping workflows, where technology enables simultaneous cross-domain working. It contrasts four case-study implementations of integrated prototyping workflow to current practice, exploring feasibility, value, and implementation challenges.

As model-based systems engineering (MBSE) is evolving, the need for evaluating MBSE approaches grows. Literature shows that there is an untested assertion in the MBSE community that complexity drives the adoption of MBSE. To assess this assertion and support the evaluation of MBSE, a principal component analysis was carried out on eight product and development characteristics using data collected in an MBSE course, resulting in organizational complexity, product complexity and inertia. To conclude, the method developed in this paper enables organisations to evaluate their MBSE adoption potential.

Within the circular economy, the Product-Service System (PSS) is a key business model to slow down and dematerialise resource consumption between manufacturers or service providers and customers. Material-Service Systems (MSSs) have been proposed as a business model to develop the performance economy focusing on the relationship between material suppliers and manufacturers. The aim of this research is to introduce and develop the MSS concept and to apply it to a real-world case study. The research involved two stages. First, a modelling method is proposed to represent the flow, transformation and ownership of resources by the stakeholders in a supply chain. Second, the method is tested using steel industrial drums as a case. The results indicate that the MSS has the potential to radically transform the steel drums industry and it could work effectively with a PSS within a reuse system to recondition and reintroduce obsolete drums into the market.

Today, product success more than ever depends on the satisfaction of consumer needs. Besides, manufacturers need to shorten development cycles and accelerate product release in order to stay competitive. This is achieved by applying customer-oriented methods allowing for fast and reliable acting. During the early phase of product development, requirements acquisition is crucial for later success of products, since specifications are most influenceable at this point. Referring to the decisive concept generation phase, material and production definition is difficult due to the highly complex interrelations between material properties, production process capabilities, and resulting product characteristics. Especially in the context of lightweight design, concurrent material and processing technology selection must be considered due to its various possible interfaces.

Thus, this contribution outlines an integrated approach towards an enhanced material and process related, customer-oriented requirements acquisition during the early phase upstream of product detailing. Here, conventional multi-criteria decision-making and tactile recognition are employed in an agile transformation model.

External sources of inspiration can promote the discovery of new ideas as designers ideate on a design task. Data-driven techniques can increasingly enable the retrieval of inspirational stimuli based on nontext-based representations, beyond semantic features of stimuli. However, there is a lack of fundamental understanding regarding how humans evaluate similarity between non-semantic design stimuli (e.g., visual). Toward this aim, this work examines human-evaluated and computationally derived representations of visual and functional similarities of 3D-model parts. A study was conducted where participants (n=36) assessed triplet ratings of parts and categorized these parts into groups. Similarity is defined by distances within embedding spaces constructed using triplet ratings and deep-learning methods, representing human and computational representations. Distances between stimuli that are grouped together (or not) are determined to understand how various methods and criteria used to define non-text-based similarity align with perceptions of 'near' and 'far'. Distinct boundaries in computed distances separating stimuli that are 'too far' were observed, which include farther stimuli when modeling visual vs. functional attributes.

Digitalization and sustainability are major challenges for today's manufacturing industry. While digitalization is characterized by the incorporation of digital technologies in the products and services as well as the value creation architectures, sustainability requires them to balance economic, environmental and social issues. In both areas, especially Product Service Systems (PSS) are constantly gaining importance. This results in so called smart PSS that integrate digital technologies as well as sustainable PSS which aim at a positive impact on sustainability. Both two concepts cannot be clearly delimited since smart PSS might be designed for sustainability as well and sustainable PSS might be used with digital technologies. This paper aims to investigate the interrelations. To that, digitalization patterns of products and services are evaluated regarding their sustainable impact. The evaluation is conducted by a survey in research and industry. Furthermore, the design of the underlying value creation architecture is investigated. Here, a methodology is proposed enabling companies to optimize their value creation architecture.

Monitoring properly the circularity performance of technical products is a point of increasing importance. Yet, evaluating the circularity potential of products during (re)design and development phases is a challenging task. In this study, several C-indicators are experienced by doctoral students and industrialists through two workshops on a real-world industrial product. The values obtained for each indicator are collected and analyzed: as all participant are working on the same technical product with the same dataset, the circularity scores calculated are compared to discuss the reliability and the uncertainty related to these indicators. These new empirical insights are put in parallel with the existing critical analyses of C-indicators from literature. As a result, future research directions on circularity indicators are advanced and discussed, including: the integration of uncertainty considerations into the assessment methodology of circularity indicators; the uptake by industry of such indicators during product design and development; the link between circularity and sustainability scores.

The purpose of this work is to compare the creative outcome in the educational context of students belonging to two different cultures, namely Singaporean and Portuguese and determine whether they respond differently to the same design brief. The participants from both samples equal 121 student designers and span from 18–25 years old. Students were randomly distributed within a uniform, standard of student performance, which allowed for fair comparison between groups. Expert judges were employed to judge the creativity of concept sketches generated during a Collaborative Sketching exercise. To evaluate the creative outcome, we employed the Consensual Assessment Technique based on a rubric-based system developed in our earlier works. The analysis of variance procedure revealed no statistically significant difference between the averaged total scores of the two groups on the appropriateness measure. However, the student designers from both samples showed statistically significant differences when provided with a baseline brief in the novelty measure. In consideration of the overall creativity scores, a relatively equivalent performance is observed across the two universities.

In the uncertain process of product development, the developer is decisively responsible for product success. He operates in a complex environment that directly influences his synthesis and analysis activities. The context of the socio-technical system of product development has already been extensively researched and defined by a large number of factors. However, the developer is described as part of the context and not as the centre, which means that many of these factors have no interaction with the developer. For the design of methods and tools that support the developer in his activities in the development process, a summarizing understanding of the influences on and by the developer is necessary. In order to create a unified understanding of the developer at the centre of product development, a Systematic Literature Review was conducted. In this article, the procedure and findings are presented. The aim was to identify factors from the literature that significantly influence the interaction of the developer in his environment. As a result, these were documented in a model, which represents the basis for further, human-centred research in the context of product development.

Data-driven design is expected to change design processes and organizations in significant ways. What actions should design managers take to ensure the best possible outcomes in this new data-driven design environment? This paper employs an interdisciplinary literature survey to distill key impacts that data-driven design may have on designers, design teams, organizations and product users. Findings reveal that designers may need a broader set of skills to be successful. For data-driven design to be most effective, design managers will be challenged with many integration tasks, including the integration of AI-based tools into design teams, the closer integration of interdisciplinary teams, the integration of qualitative design thinking methods with new data-driven design paradigms, and the integration of data and algorithms into traditional human-centred design practice, in an effort to overcome cognitive limitations and augment human skill. This paper identifies gaps in the literature at the intersection of data-driven design and design management, design thinking, and systems thinking.

In this study, we discuss a structure for developing the skills and competencies required by the learning framework of the Organisation for Economic Co-operation and Development (OECD) for future education. Given the broad range of skills and the numerous competencies required to meet the demands of future society, the proposed wider and higher-level framework is based on STEAM (science, technology, engineering, art and design, and mathematics) and addresses the limitations of conventional computational thinking by tackling some of the skills and competencies. This is done by proposing the enrichment of STEAM educational approach with art thinking, which may be defined as a creative human-centred discovery process. To explore such enrichment, we conducted a workshop on art thinking. The motivation of the workshop was to explore whether art thinking can overcome some of the limitations of computational thinking regarding future education in the OECD learning framework. We discuss STEAM as focusing on design creativity competency, and we outline the development of educational activities such as workshops to promote competencies in the perspective of OECD framework.

Truss structures are a stiff, economical, and efficient lightweight design, the limiting factor of these structures are usually the load transfer elements. This paper presents an analytical design method for optimized adhesive tubular lap joints between CFRP tubes and aluminium inserts. The analytically optimized design agrees very well with the numerical simulations and experimental results. Although the experiments show a highly non-linear behaviour, where a linear elastic correlation is expected, the total load capacity is only reached when the adhesive is fully plasticised.

Product development in today's manufacturing companies is characterized by multiple development projects under intense time constraints. This means that the success of projects impacts the company's success significantly. However, industrial practices show that many projects fail to meet their time targets. This paper presents a methodology to systematically improve project schedule adherence of development projects by combining exploratory data analysis of historic project data with project scheduling optimizations to enhance the project schedules and enable more successful projects.