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

TL;DR

The paper introduces the GenAI Workbench, a Model-Based Systems Engineering environment that integrates multimodal engineering data and AI tools to support holistic, data-driven system design workflows.

Contribution

It presents a novel conceptual framework for an open-source, AI-assisted MBSE platform that links diverse engineering data sources and automates requirements extraction and architecture generation.

Findings

Framework links semantic data from documents, geometry, and system graphs.

Automates requirements extraction and initial architecture generation.

Aims to improve integration and reduce risks in engineering design.

Abstract

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.