arXiv subjects

H. Sinan Bank

Publications and source records attributed to H. Sinan Bank.

7 recordsLinked to original sources

arXiv2026

A Framework and Prototype for a Navigable Map of Datasets in Engineering Design and Systems Engineering

The proliferation of data across the system lifecycle presents both a significant opportunity and a challenge for Engineering Design and Systems Engineering (EDSE). While this "digital thread" has the potential to drive innovation, the fragmented and inaccessible nature of existing datasets hinders method validation, limits reproducibility, and slows research progress. Unlike fields such as computer vision and natural language processing, which benefit from established benchmark ecosystems, engineering design research often relies on small, proprietary, or ad-hoc datasets. This paper addresses this challenge by proposing a systematic framework for a "Map of Datasets in EDSE." The framework is built upon a multi-dimensional taxonomy designed to classify engineering datasets by domain, lifecycle stage, data type, and format, enabling faceted discovery. An architecture for an interactive discovery tool is detailed and demonstrated through a working prototype, employing a knowledge graph data model to capture rich semantic relationships between datasets, tools, and publications. An analysis of the current data landscape reveals underrepresented areas ("data deserts") in early-stage design and system architecture, as well as relatively well-represented areas ("data oases") in predictive maintenance and autonomous systems. The paper identifies key challenges in curation and sustainability and proposes mitigation strategies, laying the groundwork for a dynamic, community-driven resource to accelerate data-centric engineering research.

cs.SE↗

arXiv2026

AI4SE and SE4AI Exploration: A Decade Looking Back and Forward

The March 2020 INCOSE INSIGHT special issue on AI and Systems Engineering (SE) became the most downloaded issue in the publication's history and launched a research community that now draws over 250 registrants to its annual workshop. In this article, we trace the progress in AI and SE across three phases (labeled here foundational, applied, and LLM inflection) based on the authors' reading of the field's core papers, and describe our opinions of where the community has converged and where critical gaps remain. Separately, a human-AI agreement literature review leveraging both human expertise and six AI models was performed to assess the relevance of 1,712 INCOSE INSIGHT articles and 889 SERC publications. The results identify five critical research gaps and offer guidance for practitioners navigating AI adoption, assurance, and workforce transformation in SE. We share the agreement data and the AI4SE/SE4AI Explorer web application so readers can compare their own relevance judgments with the human and AI raters.

cs.AI↗

arXiv2026

Design-OS: A Specification-Driven Framework for Engineering System Design with a Control-Systems Design Case

Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit and traceability from intent to parameters largely absent. Existing specification-driven and systematic design methods mostly target software, and AI-assisted tools tend to enter the workflow at solution generation rather than at problem framing. Human--AI collaboration in the design of physical systems remains underexplored. This paper presents Design-OS, a lightweight, specification-driven workflow for engineering system design organized in five stages: concept definition, literature survey, conceptual design, requirements definition, and design definition. Specifications serve as the shared contract between human designers and AI agents; each stage produces structured artifacts that maintain traceability and support agent-augmented execution. We position Design-OS relative to requirements-driven design, systematic design frameworks, and AI-assisted design pipelines, and demonstrate it on a control systems design case using two rotary inverted pendulum platforms -- an open-source SimpleFOC reaction wheel and a commercial Quanser Furuta pendulum -- showing how the same specification-driven workflow accommodates fundamentally different implementations. A blank template and the full design-case artifacts are shared in a public repository to support reproducibility and reuse. The workflow makes the design process visible and auditable, and extends specification-driven orchestration of AI from software to physical engineering system design.

cs.CE↗

arXiv2026

GENAI WORKBENCH: AI-Assisted Analysis and Synthesis of Engineering Systems from Multimodal Engineering Data

Modern engineering design platforms excel at discipline-specific tasks such as CAD, CAM, and CAE, but often lack native systems engineering frameworks. This creates a disconnect where system-level requirements and architectures are managed separately from detailed component design, hindering holistic development and increasing integration risks. To address this, we present the conceptual framework for the GenAI Workbench, a Model-Based Systems Engineering (MBSE) environment that integrates systems engineering principles into the designer's workflow. Built on an open-source PLM platform, it establishes a unified digital thread by linking semantic data from documents, physical B-rep geometry, and relational system graphs. The workbench facilitates an AI-assisted workflow where a designer can ingest source documents, from which the system automatically extracts requirements and uses vision-language models to generate an initial system architecture, such as a Design Structure Matrix (DSM). This paper presents the conceptual architecture, proposed methodology, and anticipated impact of this work-in-progress framework, which aims to foster a more integrated, data-driven, and informed engineering design methodology.

cs.SE↗

arXiv2026

A Data-Driven Analysis for Engineering Conferences: The Institute of Industrial and Systems Engineering (IISE) Annual Conference Proceedings (2002-2025)

Charting the intellectual evolution of a scientific discipline is crucial for identifying its core contributions, challenges, and future directions. The IISE Annual Conference proceedings offer a rich longitudinal archive of the Industrial and Systems Engineering (ISE) community's development, but the sheer volume of scholarship produced over two decades makes a holistic analysis difficult. Traditional reviews often fail to capture the full scale of thematic shifts and complex collaboration networks that define the community's growth. This paper presents a computational analysis of IISE proceedings from 2002 to 2025, drawing on an initial dataset of 9,350 titles from ProQuest for thematic analysis and 8,958 titles from Google Scholar for citation analysis, to deliver a cartography of the ISE field's intellectual history. Leveraging Large Language Models (LLMs) for domain-aware classification, Natural Language Processing, and Network Science, our study systematically maps thematic evolution to identify dominant, emerging, and receding research topics. We analyze citation data and co-authorship networks to uncover influential papers and authors, providing critical insights into knowledge diffusion and community structure. Through this comprehensive analysis, we establish a baseline for understanding the trajectory of ISE research and offer valuable insights for researchers, practitioners, and educators. The findings illuminate the field's intellectual assets and provide a data-informed map to guide the future of ISE. To foster reproducibility and further research, the curated dataset used in this study and the results will be made publicly available.

cs.DL↗

arXiv2026

FusionCut: Boundary Representation (B-Rep) Based and Cloud-Ready Cutter Workpiece Engagement (CWE) for Virtual Machining

Cutter-workpiece engagement (CWE) is the instantaneous contact geometry between the cutter and the in-process workpiece, playing a fundamental role in machining process simulation and directly affecting the prediction of cutting forces and process stability. The difficulty and challenge of CWE determination come from the complexity of continuously changing geometry, especially for multi-axis milling. To fulfill the requirement of generality -- for any cutter type, workpiece shape, or toolpath -- the research community has largely pursued two paths: geometrically exact solid modeling and approximate discrete modeling. The former, while accurate, has been hampered by reliance on proprietary, inaccessible software, hindering reproducibility and collaborative research. The latter sacrifices geometric fidelity for algorithmic generality, often leading to computational trade-offs. This paper presents a framework, FusionCut, that leverages the Boundary Representation (B-Rep) solid modeling kernel of an accessible, modern CAD/CAM platform Autodesk Fusion 360 -- as freely available for educational and non-commercial use. Our objective is to provide a reproducible framework for the B-Rep approach, while challenging the prevailing assumption that discrete methods such as the triangle meshes are required for general-purpose applications. By providing an accessible implementation and testing it with publicly available models and experiments, we aim to establish a baseline for what is computationally feasible and scientifically necessary for high-fidelity virtual machining. FusionCut offers a path to democratize advanced machining simulation, fostering a more open and progressive scientific ecosystem in digital manufacturing.

cs.CE↗

arXiv2026

Retrieval Augmented (Knowledge Graph), and Large Language Model-Driven Design Structure Matrix (DSM) Generation of Cyber-Physical Systems

We explore the potential of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Graph-based RAG (GraphRAG) for generating Design Structure Matrices (DSMs). We test these methods on two distinct use cases -- a power screwdriver and a CubeSat with known architectural references -- evaluating their performance on two key tasks: determining relationships between predefined components, and the more complex challenge of identifying components and their subsequent relationships. We measure the performance by assessing each element of the DSM and overall architecture. Despite design and computational challenges, we identify opportunities for automated DSM generation, with all code publicly available for reproducibility and further feedback from the domain experts.

cs.AI↗