Design Neurocognition, a field bridging Design Research and Cognitive Neuroscience, offers new insights into the cognitive processes underlying creative ideation. This study adopts a micro-perspective on design ideation by examining convergent and divergent thinking as its core components. Using 32-channel EEG recordings, it investigates how educational background (Industrial Design Engineering vs. Engineering Design) influences designers’neural activity (alpha, beta, and gamma frequency bands), behavioral responses, and perceived stress during ideation tasks. Data from forty participants reveal a consistent and meaningful interaction between brain activity, behavior, and self-reported stress, highlighting that educational background significantly modulates cognitive and neural patterns during ideation. Importantly, perceived stress shows strong negative correlations with neural power across all frequency bands, suggesting a close alignment between subjective experience and physiological measures. By integrating neural, behavioral, and psychological data, this study advances the understanding of the neurocognitive mechanisms driving design ideation and establishes a methodological foundation for bridging Design and Cognitive Neuroscience. These findings contribute to building a unified evidence base for future human-centred and neuro-informed design research.

Laser Powder Bed Fusion (LPBF) enables complex metal components for the space industry. However, as-built surface roughness affects material properties and is closely linked to design geometry. As computer-aided design tools struggle to model roughness accurately, this study explores Additive Manufacturing Design Artefacts (AMDAs) to investigate design-related roughness and its impact on fatigue performance. A space industry case study using AMDAs to replicate a 4 mm unsupported roof radius of a rocket engine component found fatigue performance reductions of 88% in horizontal builds and 65% in vertical builds compared to machined surfaces. Microstructural analysis confirmed the influence of roughness and grain structure on fatigue behaviour. Findings highlight how AMDAs provide design-specific insights and support engineers in investigating uncertainties.

Additive manufacturing (AM) enables the production of innovative, lightweight component designs in the aerospace industry. However, AM processes introduce new production feasibility considerations that must be addressed during product development. Therefore, engineers require effective design support and a new design approach to fully exploit AM’s capabilities while balancing its constraints. Through an interview study involving 20 AM aerospace industry professionals from nine countries and 10 organisations, this research identifies AM design opportunities and challenges and explores the design supports used to achieve and overcome them. The findings indicate that Laser Powder Bed Fusion is a predominant AM process in aeronautical and space applications. Further, the study identifies practical and computational design supports, describes how AM design is approached during product development and provides a model outlining a general AM design approach. Key AM design challenges identified include insufficient knowledge of material properties, limited sharing of design knowledge and a lack of understanding of the relationship between AM design and post-processing requirements. Consequently, skills gaps and educational needs for Design for AM in aerospace engineering are highlighted. Additionally, the study suggests that further AM aerospace standards, enhanced computer-aided engineering software for AM and artificial intelligence integration could improve design support.

Evaluation approaches are needed to ensure the development of effective design support. These approaches help developers ensure that their design support possesses the general design support characteristics necessary to enable designers to achieve their desired outcomes. Consequently, evaluating design support based on these characteristics ensures that the design support fulfils its intended purpose.

This work reviews design support definitions and identifies and describes 11 design support characteristics. The characteristics are applied to evaluate a proposed design support that uses additive manufacturing (AM) design artefacts (AMDAs) to explore design uncertainties. Product-specific design artefacts were designed and tested to investigate buildability limits and the relationship between surface roughness and fatigue performance of a design feature in a space industry component. The AMDA approach aided the investigation of design uncertainties, identified design solution constraints, and uncovered previously unknown uncertainties. However, the results provided by product-specific artefacts depend on how well the user frames their problem and understands their AM process and product. Hence, iterations can be required. Based on the evaluation of the AMDA process, setting test evaluation criteria is recommended, and the AMDA method is proposed.

Design thinking är ett tankesätt och en användarcentrerad metodik för att identifiera alternativ och lösningar på komplexa problem på ett kreativt och hållbart sätt.

Design thinking handlar till viss del om att tänka, men framförallt väldigt mycket om att göra och reflektera. Det handlar om att utveckla sitt kritiska, kreativa och reflexiva tänkande för att utforska och förstå användarens behov, skapa idéer och utveckla attraktiva och hållbara lösningar.

The accelerated rate of product development and design complexities offered by Additive Manufacturing (AM) has allowed for innovation in the space industry. However, the surface roughness of parts poses a challenge, as it impacts performance and is tied to design choices. Design tools for traditional manufacturing methods fall short in AM contexts, prompting the need for alternative design processes. This work proposes an experimental approach to design for AM investigation using design artefacts to explore a process-structure-property-performance relationship.

The compound nature of creativity entails the interplay of multiple cognitive processes, making it difficult to attribute creativity to a single neural signature. Divergent thinking paradigms, widely adopted to investigate creative production, have highlighted the key role of specific mental operations subserving creativity, such as inhibition of external stimuli, loose semantic associations, and mental imagery. Neurophysiological studies have typically shown a high alpha rhythm synchronization when individuals are engaged in creative ideation. Also, oculomotor activity and pupil diameter have been proposed as useful indicators of mental operations involved in such a thinking process. The goal of this study was to investigate whether beyond alpha-band activity other higher frequency bands, such as beta and gamma, may subserve divergent and convergent thinking and whether those could be associated with a different gaze bias and pupil response during ideas generation. Implementing a within-subjects design we collected behavioral measures, neural activity, gaze patterns, and pupil dilation while participants performed a revised version of the Alternative Uses Task, in which divergent thinking is contrasted to convergent thinking. As expected, participants took longer to generate creative ideas as compared to common ones. Interestingly, during divergent thinking participants displayed alpha synchronization along with beta and gamma desynchronization, more pronounced leftward gaze shift, and greater pupil dilation. During convergent thinking, an opposite pattern was observed: desynchronization in alpha and an increase in beta and gamma rhythm, along with a reduction of leftward gaze shift and greater pupil constriction. The present study uncovered specific neural dynamics and physiological patterns during idea generation, providing novel insight into the complex physiological signature of creative production.

This paper addresses the crucial need for engineering students to acquire both soft and hard skills for a successful career. While technical skills are essential, soft skills like communication, collaboration, problem-solving, leadership, and adaptability are equally vital. Traditional engineering education programs often neglect soft skill development, leaving students without structured guidance. This paper presents a strategic curriculum approach within an Industrial Design Engineering program that emphasises progressive skill development. It includes a competence profile and continuous self-assessment to encourage student reflection and growth. Additionally, a transformative process is introduced in a third-year capstone project, helping students identify and actively improve personal and interpersonal skills. The results underscore the importance of systematic soft skill development through reflective practice and assessment, offering valuable insights for engineering education.

One crucial part of education is teaching students to critically evaluate and reflect on their work. Oneway to perform this is through peer review and self-assessment. In this research paper, we present theresults of a longitudinal study over five years with 239 students following the implementation andevaluation of peer review and self-assessment. Using qualitative and quantitative analysis, we exploredifferent types of self-assessment, the benefits of incorporating self-assessment into the learningprocess, and lessons learnt during the years. Results show that students appreciate assessing their ownand others’ work. The students in the study are very good at evaluating their capabilities, the differencebetween the self-assessment and teachers’ final assessment was about 10%. With a studio-basedapproach, with formative feedback throughout the process, individual oral and written presentations andsupport from self-assessment, team feedback and teacher discussions, there is a much higher certaintythat students are assessed accurately.