This paper presents two studies with packaging design engineers and quality and risk professionals in the pharmaceutical packaging industry, addressing the critical need for design support. The studies contribute to the development of a framework aimed at balancing risk management and multi-user experience in the context of product support. A review of prior work highlights the gap in tailored support for designers in this field. Using structured interviews and thematic analysis, seven key requirements were identified to guide the framework’s creation. A user persona was also developed, capturing the core responsibilities, challenges, and motivations of quality and risk professionals. These findings provide actionable insights to aid designers address complex regulatory and user-centric challenges, paving the way for innovation and improved outcomes in pharmaceutical packaging design.

The objective of this research is to explore the use of publicly available recall data from the National Highway Transportation Safety Administration to extract Failure Modes and Effects Analysis data. This large data set was analysed using a Large Language Model chatbot. To assess the usefulness of priming the chatbot with this data, the chatbot was also asked to generate data without priming it with the recall data. This was performed on two specific products. It was found that primed-chatbot results were more specific and used technical terminology appropriate to the product being analysed. The proposed approach can be used by designers in the forward design process during new product development. The proposed approach provides designers with insight into potential failures, the associated consequences, their severity, and root causes as well.

The diverse knowledge levels among first-year mechanical engineering students lead to significant disparities in individual learning. Intelligent tutoring systems (ITS) offer a solution by providing tailored digital one-to-one instruction, bridging knowledge gaps, and equalizing learning outcomes. This thesis develops an ITS for design theory based on a knowledge-based engineering system, presenting an innovative model that integrates key features of ITS and knowledge-based systems. Implemented in a specialized environment, the system’s application and validation demonstrate its ability to meet context-sensitive design teaching requirements and provide adaptive tutoring.

The power, speed and sophistication of software for computer-aided design (CAD) drafting has revolutionized the design process and the productivity of experienced users. Assessment and mark-up of student drawings in a university class is still time-consuming and requires teaching assistants to be well-versed and proficient. This bottleneck can slow the learning of students if they are not provided with timely and proficient feedback. Software can be developed that uses the quantitative information stored in electronic files for direct comparison with a solution. This however requires an appropriate learning/teaching approach that is complementary with the assessment approach. A learning approach with complementary assessment is outlined along with the developed software for the assessment of large numbers of student submissions in a university level engineering course on drafting.

Let $\mathsf {KP}$ denote Kripke–Platek Set Theory and let $\mathsf {M}$ be the weak set theory obtained from $\mathsf {ZF}$ by removing the collection scheme, restricting separation to $\Delta _0$-formulae and adding an axiom asserting that every set is contained in a transitive set ($\mathsf {TCo}$). A result due to Kaufmann [9] shows that every countable model, $\mathcal {M}$, of $\mathsf {KP}+\Pi _n\textsf {-Collection}$ has a proper $\Sigma _{n+1}$-elementary end extension. We show that for all $n \geq 1$, there exists an $L_\alpha $ (where $L_\alpha $ is the $\alpha ^{\textrm {th}}$ approximation of the constructible universe L) that satisfies $\textsf {Separation}$, $\textsf {Powerset}$ and $\Pi _n\textsf {-Collection}$, but that has no $\Sigma _{n+1}$-elementary end extension satisfying either $\Pi _n\textsf {-Collection}$ or $\Pi _{n+3}\textsf {-Foundation}$. Thus showing that there are limits to the amount of the theory of $\mathcal {M}$ that can be transferred to the end extensions that are guaranteed by Kaufmann’s theorem. Using admissible covers and the Barwise Compactness theorem, we show that if $\mathcal {M}$ is a countable model $\mathsf {KP}+\Pi _n\textsf {-Collection}+\Sigma _{n+1}\textsf {-Foundation}$ and T is a recursive theory that holds in $\mathcal {M}$, then there exists a proper $\Sigma _n$-elementary end extension of $\mathcal {M}$ that satisfies T. We use this result to show that the theory $\mathsf {M}+\Pi _n\textsf {-Collection}+\Pi _{n+1}\textsf {-Foundation}$ proves $\Sigma _{n+1}\textsf {-Separation}$.

The notion of “zero-sum” games describes situations characterized by scarcity and competition. Reframing these situations to yield more positive sum scenarios benefits from design strategies that can identify new sources of value, oriented toward out-comes that reward collective benefit over maximizing one’s individual strategy to win at others’ expense. This emergent practice, called Positive Sum Design, identifies and critiques the conditions that contribute to zero-sum bias in the interest of creatively reframing challenges and redesigning scenarios to encourage more cooperative strategies and pluralistic values. Positive Sum Design can be thus characterized as both a cultural critique and a set of creative practices applied toward that critique to help practitioners identify opportunities for transcending presumed constraints and transmuting zero sum games into non-zero-sum ones.

This paper examines the effects of prototyping on design space exploration (DSE). Based on a literature review, a study design is proposed that attempts to integrate a longitudinal view from downstream development steps in the point-in-time investigation of design fixation. This study design is tested in a pilot study, the results are presented and discussed. The observation of participants’ design fixation (DF) in downstream activities shows that the need to create prototypes limited DSE behaviour, and suggestions for further adaptation of the study design are made. Challenges related to group dynamics, bias and logistical issues highlighted the need for a more refined study design. The findings highlight the role of prototyping in limiting DSE behaviour and suggest improving metrics, refining interventions and using structured moderation to improve future DF and DSE research.

This paper introduces a novel approach to analysing design protocols using a combination of methods. It describes an approach using a synthesis of concept extraction (using an LLM), semantic analysis (using word vectors and conceptual clustering), and network analysis (following graph construction). It suggests that the resulting measures are useful for studying design framing and for aiding qualitative analysis. It demonstrates this technique with data from a study of 17 designers addressing two design problems. The method enables the comparison of designers working on the same problem as well as the study of individual designers’ use of concepts over time during a think-aloud study.

Sensor-integrating, “gentelligent” components allow to “inherit” operational loads-data for design optimisations from one generation to the next. For area-wide acquisition and reliable transmission of this data, wireless sensor networks (WSN) are often used, but small sensor nodes for reconstructing deformations and loads, so-called shape sensing, are rarely considered as well as a methodical development of such sensor nodes. This paper presents the development of a small sensor node in accordance to the VDI 2206 for shape sensing with a prototype with strain gauges, HX711 A/D converters and an Arduino Nano 33 IoT microprocessor. An infrastructured WSN is built and tested on an aluminium part at a test rig. The shape sensing is carried out with three sensor nodes and the deformed shape is displayed on a server-website to demonstrate the functionality of the methodically developed WSN.

Radiotherapy involves applying radiation doses to tumor cells and healthy tissue. To protect healthy tissue, an accessory called a bolus is used. Traditional boluses face issues such as limited adaptability and inconsistencies in radiodensity. This study proposes a low-cost process that uses 3D scans and additive manufacturing (AM) to design and produce custom boluses. The method uses a 3D scanner as an alternative to standard medical image acquisition, processes the images with CAD and mesh optimization, and then manufactures the pieces through additive manufacturing using polylactic acid (PLA) as the printing material. By optimizing the fill percentage, radiodensity was controlled, resulting in boluses that achieved a 65% cost reduction in material and an 81% savings in imaging compared to the traditional method.

Among state-of-the-art research, thermoelectric modules using the Peltier effect are used for general personalized climatization. However, none of the personalized climatization approaches found in literature reviewed the usability for the wearer, let alone in the context of motorcycle driving. This work was aimed at integrating Peltier technology into a motorcycling protective item in such a way that it is functional, perceived as safe, and usable for motorcyclists. Multiple integration options observing the requirements for motorcyclist’s safety equipment were generated. The functionality and usability of the integration approaches, as well as their impacts on perceived safety of the driver were evaluated. This work could serve as a base for future studies addressing user-oriented methodologies for the validation of technical products in the context of motorcycle protective clothing.

Substantial engineering efforts are dedicated to reducing injury risks in crash scenarios during the development of new vehicles. This is achieved by performing crash simulations to optimize the nonlinear behavior of systems. However, the complexity makes their behavior difficult and time-consuming for engineers to understand. To reduce the analysis time, this study introduces a modular framework combining Explainable Artificial Intelligence and Large Language Models (LLM). Shapley Additive Explanation values allow for simulation-wise feature importance attribution and generate a data-driven understanding. An LLM assists by making result data interactively accessible and supports technical report generation. Validated through a real-world vehicle side crash optimization use case, the framework demonstrates enhanced and accessible insights into system behavior within virtual engineering.

How can local governments in developing countries, constrained by limited resources, identify and respond to the most pressing public demands? This paper posits that public deliberative platforms, even those with controlled agendas, can be instrumental in this regard by facilitating communication between local elites and ordinary citizens, thereby leading to an observable uptick in political trust over time. Public deliberation serves two functions: firstly, it highlights shifting societal issues, incentivizing bureaucrats to respond more promptly; and secondly, it generates narratives that temporarily improve the public perception of local governments, even among individuals not directly benefiting from government actions. This study provides evidence consistent with these theoretical implications by examining Chinese topical debate programs, during which local officials engage with citizens and respond to their concerns. Empirical results based on a staggered difference-in-differences design suggest that broadcasting such programs in China produces a prompt increase in citizens’ trust in local officials. Our results demonstrate that public deliberation can yield noticeable outcomes in developing countries, even with controlled agendas and constrained resources.

This study explores the integration of network analysis and CAD/PDM log data to analyze collaboration and activity patterns in a multi-year engineering project. Using logs from a collaborative CAD platform with PDM features, the research examines team interactions and network evolution over time. Key findings reveal that early project stages featured smaller, denser networks, while later stages saw larger, less interconnected structures. Subteam formations were dynamic, with variations in size and number. Individual-level analysis showed that user influence, measured through eigenvector centrality, did not always align with activity volume. This work highlights the potential of CAD/PDM data for understanding collaboration dynamics and lays the groundwork for further studies on team interactions in design processes.

Design activities rely on external representations to offload cognitive effort and communicate ideas. These representations, ranging from sketches to virtual reality (VR), influence cognitive processes and perceptual outcomes. This study investigates the impact of different media representations on brain activity by comparing neural responses to design representations in VR and desktop monitor conditions. Utilizing brain network analyses derived from EEG signals in alpha, beta, gamma, and theta bands, results demonstrate that VR elicits greater cognitive integration and sensory engagement. These patterns suggest that VR facilitates holistic evaluations, while desktop representations support precision-focused tasks. These findings provide actionable guidance for optimizing design media selection based on cognitive objectives and contribute to the emerging design neurocognition field.

People rely on daily interactions with artifacts, greatly influencing their physical, mental, and social well-being. Despite this, current design practices often overlook well-being as a core consideration. Affordance theory, which explains how an artifact’s features enable specific user actions and experiences, offers a promising lens for addressing this gap. This study focuses on assessing affordance mechanisms as a potential tool to support design practices to design for positive well-being outcomes. Using transportation modes as a case study, we interviewed college students to explore how specific mechanisms can contribute to positive or negative well-being outcomes. Findings resulted in 233 examples, which showed trends in mechanisms, modes, and well-being outcomes. Ultimately, this work presents an initial framework for embedding well-being considerations into design.

This paper explores the multifaceted concept of design theories value, challenging traditional views of science and philosophy and proposing a novel framework for evaluation. Through critical analysis, considering design theories like C-K theory, PSI, GDT, and CDP, and insight from the history of science, we establish the need for a new value model of design theories that includes design-related and other general properties such as generativity, robustness, and impact on practice. We adapt a recently developed system value model (SVM) to consider the diverse perspectives of design theory stakeholders. Our framework is tested on the PSI theory, demonstrating its applicability. This paper redefines how we perceive and measure the value of design theories, offering insights that could influence future research and practice in design science.

This systematic literature review comprehensively assesses the risks associated with implementing Industry 4.0/5.0 technologies. It clusters these risks into six groups (strategic, financial, operational, technological, environmental, and sociocultural). Using a PRISMA-guided approach, the analysis of 83 peer-reviewed papers identified 36 unique risks out of a total of 811. The findings reveal critical challenges, including in cybersecurity threats, financial burdens, technological obsolescence, and workforce adaptation. These results provide a structured risk categorization that can assist enterprises, in effectively mitigating risks and aligning their strategies with Industry 4.0/5.0 transitions. This framework closes knowledge gaps and offers actionable insights for a robust and sustainable implementation.

Products need to be developed faster and more efficiently, which is why companies are seeking to leverage the benefits of digitalization. A current trend is the digital twin (DT), which offers many advantages but also involves high development efforts. Research has addressed the use of the DT along the product life cycle (PLC) to compensate for the development effort, but these approaches are often imprecise and not directly applicable in industry. This paper therefore describes how the individual components of the DT can be utilized along the PLC beyond the manufacturing and use phase with a focus on product design. The resulting framework is then illustrated using a case study of a product service system. This article aims to facilitate the use of the DT in industry to improve product design across product generations.

To specify the solution principles of a design, a material selection should be performed already in the concept phase. Based on the design constraints, inappropriate materials are removed using an attribute filter. Brittle materials are often removed using fracture toughness attribute limits, but this does not take into account the strength specific stress level and incorrectly excludes entire classes of materials. We propose a novel filtering method to account for brittle failure in material selection. Based on linear elastic fracture mechanics, we establish a relationship that correctly describes the transition between brittle and ductile materials. Representing the proposed filter on an Ashby plot, we evaluate its effect on the further material selection process. Additionally, we show how different defect sizes in the materials can be incorporated into the filtering process.