cad flow 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.

In hardware development, 3D CAD models exhibit highly complex dependency structures—often comprising thousands of components—leading to significant challenges in impact analysis, cross-role collaboration, and synchronization. This complexity exposes nine critical issues spanning traceability, navigability, and consistency. To address this gap, we conducted a thematic analysis of 100 online forum discussions and semi-structured interviews with 10 senior hardware designers, systematically identifying and categorizing these pain points for the first time. Building on these findings, we propose the “dependency-aware collaboration” design paradigm and introduce a corresponding framework featuring dependency visualization, change-propagation alerts, and contextual synchronization for collaborative editing, guided by six design principles. Our work fills a theoretical void in CSCW research on hardware co-design and provides empirically grounded, actionable guidelines for next-generation CAD collaboration tools.

This study addresses the persistent challenges of version control in modern computer-aided design (CAD), where data complexity and strong interdependencies hinder effective implementation, thereby limiting design traceability, variant management, and team collaboration. Through qualitative content analysis, the authors systematically coded and synthesized insights from 170 online forum posts, revealing recurring sociotechnical challenges that CAD users face in version management, continuity, scoping, and distribution. The work introduces “infrastructural reflexivity” as a novel design principle for CAD tools, emphasizing support for coordinated work and cross-boundary collaboration. This concept offers actionable guidance for software developers and opens new research avenues for reimagining version control systems in complex design environments.

This study addresses the lack of standardization and poor reproducibility in data generation for machine learning modeling of three-dimensional obstructed channel flows. We propose a configuration-driven, end-to-end automated framework integrating parametric CAD modeling, signed distance field (SDF)-based voxelization, high-fidelity lattice Boltzmann simulations using waLBerla, and multi-resolution tensor-based registration—all orchestrated via Hydra/OmegaConf to enable fully configurable pipelines and systematic ablation studies. Our key contributions are: (1) the first standardized data generation paradigm specifically designed for obstructed flows, supporting joint geometric–flow-field parameterization; and (2) a large-scale, high-quality 3D flow dataset comprising over 10,000 samples spanning Reynolds numbers Re = 100–15,000. The dataset demonstrates superior storage efficiency and empirical effectiveness in training physics-informed models (e.g., 3D U-Net), significantly enhancing reproducibility and generalizability in physics-guided machine learning.

本文提出一种架构，通过分离意图解释、执行和解释，并基于领域本体约束分析链，解决了LLM辅助科学可视化中生成错误脚本的问题。

Existing CAD generation methods struggle to simultaneously preserve modeling history, topological reference stability, and feature-level editability in cross-platform scenarios. This work proposes CADIR—an agent-oriented, executable intermediate representation that explicitly constructs a procedural graph encompassing operation sequences, parameter dependencies, constraints, and topological selections based on the OpenCASCADE (OCCT) geometric kernel. To enable faithful cross-platform model reconstruction, CADIR introduces a geometric signature matching mechanism. It is the first approach to support explicit procedural graph representations that allow editing across heterogeneous CAD backends. By integrating text- or image-driven procedural graph retrieval, CADIR demonstrates high-fidelity, editable reuse of complete models and substructures across FreeCAD, SolidWorks, and Fusion 360, enabling seamless subsequent modifications.

本文提出CIT-CAD框架，通过构建约束意图树来指导CAD代码生成及验证，解决现有方法忽视设计结构与关系错误的问题。

We introduce autonomous, intent-preserving Design for Manufacturing (DFM) redesign of CAD parts: given an engineer's CAD model, the method returns a variant that is easier to manufacture without losing its design intent. Generating such a redesign in a single shot is unreliable, since CAD fidelity degrades as parts grow complex; we instead produce it as a sequence of individually verified design transitions. Our DFM-Redesign pipeline realizes this with two coupled agent subsystems driven by a pretrained multimodal LLM: a DFM Reviewer that inspects the current design and proposes one intent-preserving manufacturability improvement at a time, and a CAD Modifier that executes each proposal as an edit to the part's CadQuery program. The CAD Modifier closes a verification loop, compiling every candidate edit and visually checking it against the intended change from multi-view renderings, then re-generating or re-instructing until the edit is accepted or abandoned. Iterating review and verified modification compounds edits into parts more complex than one-shot generators reliably produce, preserves the original intent at each step, and requires no fine-tuning. On a 46-part benchmark scored by chamfer distance to reference geometries, the CAD Modifier reproduces target parts more accurately on average than chain-of-thought single agents given the same tools, and ablations isolate the contributions of the visual review loop and of captioning the design state before each edit. A centrifugal pump casing built from 32 chained transitions illustrates the complexity reachable by compounding verified edits. This is a preliminary report: evaluation of the full review-and-redesign loop, including manufacturability gain and an operational measure of intent preservation, is ongoing.

本文探讨了大型语言模型在电子设计自动化中的角色转变，从生成器到协调器，并提出需要一个标准化、物理感知的协调器来解决现有方法难以扩展到工业设计的问题。

Existing approaches rely on tool-level graph representations of historical trajectories, which struggle to generalize to new tool sets and thereby limit the planning capabilities of large language models. To address this, this work proposes a Functional-level Workflow Graph (FWG) that abstracts tool-specific behaviors into functional-level workflows through trajectory uplifting, effectively decoupling workflow planning from tool selection. The framework incorporates a source-gating mechanism and skill-specific rewards, combined with reinforcement learning, to ensure reliable and traceable data flows. Evaluated on two in-distribution and three out-of-distribution benchmarks, the method significantly outperforms current state-of-the-art approaches and demonstrates strong cross-domain generalization to unseen tool sets.