design validation and case studies

Industrial Control and Automation Software (iCAS) suffers from ambiguous design concepts and a lack of foundational design theory, leading to empiricism and inconsistent engineering practices. Method: This study systematically introduces design theory to iCAS for the first time, establishing a scientifically grounded design-definition framework. Through interdisciplinary literature review and theoretical analysis, it clarifies the ontological boundaries, core semantics, and quality criteria of design; rigorously distinguishes “design activity” from “design language”; and proposes a novel design balance mechanism reconciling system evolvability with real-time operational constraints. Contribution/Results: The work yields a reusable design metatheory for iCAS software engineering, enhancing design process standardization, traceability, and innovation capacity. It advances the field from experience-driven practice toward theory-guided development, providing principled foundations for systematic design methodology and implementation pathways.

Existing AutoML systems for novice users prioritize algorithmic sophistication over usability, trust, and interpretability, hindering effective adoption. Method: This paper proposes an end-to-end abstracted pipeline tailored for novices, spanning data ingestion, guided configuration, training, evaluation, and inference. Grounded in four human-centered design principles—ensuring first-model success to boost self-efficacy, providing explanations to foster accurate mental models, applying contextual abstraction to maintain the zone of proximal development, and enhancing predictability and safety to strengthen perceived control—we employed user-centered design to develop a prototype and conducted a controlled 24-participant user study. Contribution/Results: UEQ (User Experience Questionnaire) results confirmed that all participants successfully built models, with significantly positive ratings for usability, trust, and comprehensibility. Domain-expert evaluations further corroborated the effectiveness of the design principles in supporting novice skill development.

Early implicit assumptions about materials and fabrication processes in CAD design often lead to late-stage design lock-in that is difficult to rectify. To address this, we propose a modular, extensible, interactive workflow exploration architecture that enables designers to execute, preview, and compare multiple fabrication processes in real time during CAD modeling. Methodologically, we unify empirical craft practices with academic manufacturing knowledge for the first time—via abstract workflow interfaces, CAD-model-driven process simulation, and collaborative design research. Our implementation reproduces five representative fabrication techniques, captures practices from six expert artisans, and extends three literature-based workflows. A design workshop evaluation demonstrates that the tool significantly broadens creative exploration and deepens procedural understanding of fabrication. By embedding fabrication awareness directly into the CAD environment, our approach advances co-design paradigms toward manufacturability-aware design.

This work addresses the lack of systematicity in engineering system design, often caused by ambiguous requirements and poor traceability, as well as the prevailing focus of existing AI tools on solution generation rather than problem formulation. To bridge this gap, we propose Design-OS—a lightweight, specification-driven five-stage design process that ensures end-to-end traceability from conceptual to parametric representations through structured design artifacts. For the first time, we extend specification-driven human-AI collaboration from software to physical system design, integrating control theory with systems engineering principles. The framework enables human-AI co-execution via autonomous agents within a unified, auditable, and hardware-agnostic workflow. We demonstrate its generality and reproducibility on two rotary inverted pendulum platforms, with open-sourced templates and complete design artifacts significantly enhancing transparency and systematic rigor.

This study addresses the insufficient automation capability of Model-Driven Engineering (MDE) and the diminishing role of engineers in the AI era. Methodologically, it synthesizes metamodeling, AI-assisted modeling, model transformation, formal verification, and human factors engineering to establish a human–machine collaborative modeling paradigm, featuring multi-granularity model coordination and real-time feedback mechanisms. Key contributions include: (1) the first systematic identification of three fundamental bottlenecks—semantic gap, dynamic adaptability, and trustworthiness with explainability; (2) the distillation of six emerging engineering activities requiring automation across the full system lifecycle; and (3) the proposal of a theoretically grounded, industrially viable framework and research roadmap for intelligent MDE evolution, which balances automation efficacy with sustained engineer agency and domain expertise.

This study addresses the persistent challenges of inefficiency and inconsistent design fidelity that developers encounter when translating high-fidelity mockups into production-grade user interfaces. Through controlled experiments conducted across Angular, iOS, and Android platforms in an industrial setting, the work presents the first empirical evaluation of an AI-assisted development tool integrated with a design system. The findings demonstrate that this approach substantially enhances both development efficiency and design consistency: delivery time was reduced by 46.7%–69.4%, task completion rates improved, performance variability decreased, and workflow friction was markedly alleviated. These results validate the synergistic value of design-system-aware AI tools in enabling automation and standardization across multi-platform front-end development workflows.

This study addresses the challenge faced by production system engineers in automatically verifying production line layouts due to limited knowledge of PDDL and planning theory. To bridge this gap, the authors propose a novel approach based on an Asset Administration Shell (AAS) capability model that natively generates complete PDDL planning problems directly from domain-level descriptions, eliminating the need for PDDL-specific submodels. The method integrates four Industry 4.0 standards—VDI 3682, IEC 61360-1, IDTA 02011, and IDTA 02016—to construct the AAS and employs an extraction algorithm to automatically translate multi-AAS architectures into PDDL domains. In a laboratory case study, the approach enabled engineers to systematically compare four layout variants by modifying only the AAS model, significantly lowering the barrier to adopting automated planning in industrial settings.

本文通过结合大语言模型和确定性工程后端，使用BESO和PSO优化方法解决复杂物理工程设计问题，实现自动化且可认证的工程设计。

This study addresses the inefficiencies and impeded knowledge transfer arising from fragmented verification and validation (V&V) practices at the Jet Propulsion Laboratory (JPL). To overcome these challenges, this work proposes a unified V&V architecture grounded in human-centered design. By decoupling methodologies while maintaining a common attribute set, the architecture achieves bidirectional traceability through relational design and platform-independent SysML modeling. Furthermore, it establishes a comprehensive toolchain by integrating the Jama platform, modular templates, and digital thread technologies. This research effectively balances engineering rigor with agility, facilitating process automation, pattern reuse, and efficient cross-project collaboration. Ultimately, it provides a scalable and unified paradigm for the V&V of complex systems.

This study addresses the limitations of existing SysML verification approaches, which are often tool-dependent and restricted to performance properties, lacking support for automated validation of behavioral and interface requirements. To overcome these shortcomings, this work proposes a tool-agnostic, automated verification workflow driven by SysML test cases, integrating UML Testing Profile and behavioral diagram constructs to enable unified validation of multidimensional attributes—including behavior, timing, and state responses. The methodology was developed through a mixed-methods research strategy combining literature review and stakeholder interviews, and its efficacy was empirically validated across two independent SysML toolchains. The approach not only transcends the constraints of conventional parametric methods but also enables automatic traceability of verification results back to the original model elements.