product design

This study addresses the challenge of transforming stakeholder requirements into product requirements in software-driven automotive systems. Leveraging a dataset of 8,082 stakeholder requirements and 5,870 product requirements provided by Infineon, the research employs a hybrid methodology integrating structural statistics, decision modeling, traceability mining, textual analysis, and hardware-software linkage to systematically analyze the requirement refinement process. It reveals, for the first time, that requirement complexity primarily stems from ambiguous architectural scope and missing contextual information rather than linguistic redundancy. The work establishes a classification framework for mapping stakeholder to product requirements, identifies systematic differences across abstraction levels, and proposes key improvements in requirement validation, deviation management, and contextual tooling to support efficient and reusable automotive development.

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.

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.

In industrial cyber-physical systems (ICPS) research, demonstration objectives are often ill-defined, and technical feasibility assessment is frequently decoupled from outcome validation. Method: This paper proposes a five-level demonstration framework grounded in Maslow’s hierarchy of needs, systematically mapping demonstration goals to concrete research tasks and industrial use cases. It explicitly links work packages, verification metrics, and real-world scenarios, overcoming the vagueness and low operationality inherent in conventional Technology Readiness Level (TRL) frameworks. The approach integrates requirements engineering, modeling of software-intensive systems, and hierarchical framework design to support cross-phase requirements evolution analysis. Contribution/Results: Applied in two ICPS research projects, the framework effectively identified demonstration misalignments, refined requirement specifications, and significantly enhanced the precision, consistency, and rigor of feasibility assessment and project planning.

In software design, paradigm-implied semantic expectations—such as data abstraction consistency and feedback-control closed-loop behavior—are often left implicit, leading to design deviations and verification challenges. To address this, we introduce the concept of *design obligations*: explicit, logically formalizable, and verifiable specifications that codify such implicit constraints inherent to design paradigms. Leveraging formal modeling and paradigm semantics analysis, we establish two obligation frameworks—one for data-abstraction-based systems and another for feedback-driven adaptive systems—precisely capturing their core semantic requirements. We demonstrate that common design flaws stem from obligation violations and show how these obligations enable rigorous compliance verification and pedagogical application. This work bridges the semantic gap between design intent and implementation, providing both theoretical foundations and a methodological framework for paradigm-driven design assurance.

This study addresses the challenge in axiomatic design of accurately translating customer needs and constraints into a minimal and independent set of primary functional requirements (FRs). Focusing on the problem definition phase, it systematically elucidates the nature, invariance, and formulation principles of primary FRs. Building upon Nam P. Suh’s theoretical framework and integrating insights from complexity theory and requirements engineering, the work establishes—for the first time—the objectivity and uniqueness of primary FRs, clarifies common misconceptions, and critically examines the applicability boundaries of large language models in this context. The research provides designers with a clear, actionable methodology for constructing primary FRs, thereby significantly enhancing the rigor of problem definition and the likelihood of successful design outcomes.

This work proposes a systematic approach to derive task effectiveness requirements in the absence of explicit user needs. The method deconstructs task intent into context, functionality, constraints, critical dimensions, performance attributes, and architectural solutions, and introduces a task complexity factor to quantify the impact of external challenges and technology maturity. By integrating Best-Worst Scaling, it prioritizes critical dimensions based on stakeholder judgments. Through task decomposition modeling and quantitative complexity analysis, the framework supports integration with UAF/SysML artifacts and establishes a traceable mechanism for generating Tier 1 and Tier 2 requirements. The approach is validated using a close air support mission case study, effectively addressing a critical gap in requirements engineering when clear initial inputs are unavailable.

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.

Industry 4.0 drives deep integration of cyber-physical systems and service ecosystems, necessitating a paradigm shift for industrial designers—from discrete product design to cross-system integrative design—yet their evolving professional roles and requisite competencies remain inadequately defined. Method: Through field research in Chinese smart manufacturing enterprises and comparative analysis of design education models, this study identifies core competency requirements for industrial designers. Contribution/Results: It proposes a four-dimensional competency framework encompassing systems thinking, digital technology application, service integration, and collaborative innovation. The study introduces the novel professional identity of the “systems-integrative designer” and develops an embeddable curriculum pathway and pedagogical reform strategy for higher education. These findings provide both theoretical grounding and actionable guidance for transforming industrial design education and optimizing industry-aligned talent development.