Manufacturing companies engineering Cyber-Physical Systems of Systems face growing challenges in maintaining consistent product-related data and processes. This paper proposes a PLM Architecture Framework that integrates concepts from Product Lifecycle Management (PLM) and Enterprise Architecture (EA) along an adapted DevOps lifecycle. The framework enables consistency and transparency across engineering and business domains. A case study in machine tool engineering illustrates its potential to enhance data traceability, stakeholder alignment, and cross-generational digital continuity.

Trifludimoxazin + saflufenacil is a premix protoporphyrinogen oxidase-inhibiting herbicide with both preemergence and postemergence activity pending registration in the United States for burndown application ahead of planting corn, soybean, grain sorghum, and wheat. With early-season corn growth often occurring simultaneously with preplant burndown of fields in later-planted soybean and grain sorghum, the odds of negative impacts associated with off-target herbicide movement to the crop increase. Field studies were conducted in 2024 at seven different sites to evaluate the effect of reduced rates of trifludimoxazin + saflufenacil (12.5% to 0.4% of the lowest proposed labeled application rate of 38.3 g ai ha-1) applied to 2- or 4-leaf (lf) corn. Corn injury 7 days after treatment (DAT) for the early and later application timings ranged from 21 to 3% and 19 to 5%, respectively, while at 14 DAT these respective ranges were 12 to % and 8 to 1%. At 28 DAT, the highest rate (1/8x) applied to 2-lf corn resulted in 6% visible injury while no other treatment surpassed 3% injury. Early season plant height was negatively impacted, more so at the three highest rates applied and at the earlier timing, but this reduction did not impact yield, therefore application of trifludimoxazin + saflufenacil adjacent to corn in the early vegetative stages of growth should be avoided. However, corn affected by off-target movement should be expected to fully recover following 2-lf exposure, with no impact on yield and minimal yield impact at later timing (<3% reduction), given adequate growing conditions and agronomic/pest management practices.

Current generative artificial intelligence for Computer-Aided Design (CAD) optimizes for geometric similarity, neglecting engineering criteria like functionality, manufacturability, and sustainability. This paper addresses this gap and proposes a conceptual framework to reorient generative CAD from replicating shapes to achieving function. We introduce two hybrid training strategies: a pre-learning approach using synthetically labeled datasets (evaluated via FEA, CAM, LCA) and a self-learning approach where GenAI uses these knowledge-based tools as a reinforcement feedback loop.

In iterative product development, teams encounter various issues, such as difficulty communicating easily with stakeholders during reviews or internally during sprint planning. The present paper proposes a product-oriented visualization method that highlights engineering changes and deviations, enhances communication, anchors review feedback directly to components, and supports deriving actionable planning steps. Its implementation in development settings has demonstrated the enhancement of transparency, shared understanding, and traceability.

Perpetual innovative products (PIPs) enable the reuse of components from previous generations to create new products with improved functionality and performance, supporting a circular economy. However, the concept entails uncertainties in design due to degradation and functional integration. This paper examines how testing can reveal and reduce these uncertainties through the analysis of testing activities. A four-step process is proposed that integrates testing in PIP development. The process strengthens decision-making by translating heterogeneous testing into actionable design knowledge.

Long-COVID is a complex, multi-system condition with variable care across the UK. Using a systems and design engineering approach underpinned by Model-Based Systems Engineering (MBSE), this study examined Long-COVID clinic pathways through semi-structured interviews with 15 clinicians and patients. Thematic analysis identified five domains—attitudes, relationships, service integration, technology adoption, and safety netting. The final synthesised swimlane diagram revealed opportunities to improve coordination, operational efficiency, and patient safety within evolving care models.

This study explores the alignment of production system development processes (PSDP) with agile principles in the automotive industry. A multiple case study of eight companies reveals low overall alignment, with OEMs and subcontractors constrained by sequential, stage-gate structures, while engineering consultants show higher agility in early phases. Findings suggest gradual adoption of hybrid models, culural changes and iterative planning to enhance flexibility and responsiveness.

This paper investigates how advanced manufacturing firms mature technologies within mission-driven ecosystems. Two multi-partner case studies show how the design across six innovation dimensions—purpose, strategy, leadership, governance, innovation process, and budgeting & planning—enable co-maturation of novel technologies. Findings demonstrate that strategic partnerships with a shared mission, dual iterative/linear processes, and aligned governance accelerate mission-driven innovation from idea to scaled implementation.

This paper proposes a methodological approach for designing smart composite hydrogen tanks using strain gauges and finite element analysis for continuous structural health monitoring. Simulation identifies critical stress points to optimize sensor placement. Laboratory burst and cyclic tests provide a baseline and proof of concept for a remaining useful life analysis, improving safety and resource efficiency in hydrogen storage. Results demonstrate that strain data reflect stress patterns and material responses, supporting effective monitoring of tank condition during pressurization cycles.

Product development increasingly integrates generative AI tools to enhance creativity and efficiency. However, their actual impact on structured design work, particularly on method application and resulting designs, is not well understood. This study examines the effect of (1) method application quality on (2) product concept quality, influenced by (3) potential confounders like AI usage. Statistical analysis reveals that method application quality correlates positively with product concept quality, while higher AI usage correlates negatively with both, indicating limitations in AI usefulness.

Project-based learning is a key format in engineering design education. However, many students face difficulties due to a lack of prior knowledge required for successful project participation. To address this issue, we developed e-learning content to support students during the self-study time in preparation for the project work. This paper presents an evaluation of the impact of the e-learning content when used alongside project-based activities. The results indicate increased student confidence in developing mechatronic systems and a positive effect on acquiring professional competencies.

This study presents a structured approach for developing new modular, size-variable product families in small and medium-sized enterprises (SMEs), demonstrated through a case study on air filtration units. Starting from a minimum viable product (MVP), the approach provides a framework for size level definition and systematic generation of alternative modular concepts while considering product-specific design trade-offs. An evaluation combining qualitative criteria assessment with quantitative cost forecasting enables transparent concept comparison.

This paper argues that disagreements about what counts as an ideal prosthesis arise from tacit framings of disability. Drawing on Wittgenstein’s language games within an abductive–deductive–inductive approach, we identify four prosthetic language games: medical, social, relational, and critical. By rendering their distinct grammars explicit, the framework reframes interdisciplinary disagreement as epistemic plurality and supports boundary work, translation, and epistemic fluency, enabling more reflective and dialogical design research without collapsing plural standards of prosthetic success.

While studies on generative artificial intelligence for product development have gained momentum, they consistently report recurring challenges. To synthesize these obstacles, we surveyed 1074 papers, resulting in a taxonomy of 27 distinct barriers. The study analyzes their frequency, discusses their interrelations, and contextualizes their root causes. Our findings show that model capability, output validity, and user trust are the most dominant obstacles, while aspects like environmental concerns are often overlooked. The study concludes with recommendations for research and practitioners.

Digital user tests utilizing musculoskeletal human models facilitate ergonomic assessments in the early phases of the product development process. In the underlying posture prediction models, the various movement strategies of the users need to be represented. Behavior cards are an evaluated tool for the representation of such movement strategies; however, a standardized determination of behavior cards is lacking so far. This study explores a cluster-based and a regression-based method for standardized behavior card determination, demonstrating the applicability of both methods.

A key aspect of Circular Economy (CE) is focusing on value creation through customer functionality and service across the entire product life cycle, supported by digitalization tools for improved management. This shift has led to the rise of smart Product-Service Systems (PSS) models. However, designing smart PSS is complex, requiring methodological support for successful implementation. This study explored the feasibility of a novel tool based on the Quality Function Deployment (QFD) framework through its practical application in the photovoltaic industry.

This paper proposes a conceptual framework for integrating design methods into Human–AI Co-Creation, redefining methods as mediating structures between task context, procedural logic and results. Two examples illustrate how AI supports both method execution and method selection under varying autonomy levels. The framework highlights implications for transparency, traceability, responsibility and method education, and offers a structured basis for analysing, teaching and developing method-based design practice in AI-rich environments.

Currently, the research on the key factors which affect clinical and non-clinical pregnancy in high-quality single blastocyst transfer cycles remains relatively limited. This is particularly true for FET cycles, where the relationship between the transfer of high-quality single blastocysts and pregnancy outcomes has not been fully explored. This study aimed to identify key factors influencing clinical pregnancy outcomes in high-quality single blastocyst frozen-thawed transfer cycles to optimize assisted reproductive technology (ART). Patients under 38 years old who underwent high-quality single blastocyst frozen-thawed embryo transfer were included. Based on clinical pregnancy outcomes, they were divided into clinical pregnancy (Group A) and non-clinical pregnancy (Group B) groups. Key influencing factors were analyzed to guide the selection of blastocysts with the highest pregnancy potential.The result showed that Group B showed significantly higher age and infertility duration, but lower AMH levels, antral follicle count, and endometrial thickness on the day of transfer compared to Group A (P < 0.01). Infertility type also differed significantly (P < 0.01). Blastocyst grading differed between groups (P < 0.01), while E2, LH, P levels, embryo age, and D3 cleavage-stage cell count showed no significant differences (P > 0.05). Multivariate analysis revealed that infertility type, age, infertility duration, and endometrial thickness significantly impacted clinical pregnancy outcomes (P< 0.05), while AMH, antral follicle count, and blastocyst grading had no significant effect. All in all, clinical pregnancy outcomes are significantly influenced by age, infertility type, infertility duration, and endometrial thickness. Early treatment, optimized endometrial conditions, and selecting high-quality blastocysts are recommended to improve pregnancy rates.

Autonomous driving is a promising technology for public transportation to solve two main challenges: The driver shortage and the reduction of environmental impact. This contribution investigates if already existing requirements in standards, laws, regulations and guidelines for accessibility, safety, security and bus drivers’ tasks of transit buses in Europe and Germany can also be complied to with an autonomous transit bus or if the requirements need an adaption. 54 impactful requirements on autonomous transit buses have been found and their impact and opening design space will be discussed.