AI 模型和平台
OpenAI 从设备到车辆的物理工程试验
在 Google 上的首选来源中添加 Unite.AI
OpenAI 宣布是2026年11月3日的一套物理产品工程项目，包括一个Astra设计的测试装置，与奇普·加纳西拉辛(英语：Chip Ganasi Racing)的扩大工程工作，以及与马兹达新的车辆设计研究协作。
能力跨越机械和电气工程以及端到端的系统设计，从准备图纸和设置模拟到协调各学科的变化。 目的是给工程师更多的时间探索想法和改进设计，同时将各种专门工具的工作联系起来。
端到端设备实验
设备实验测试了GPT-6 Astra的多模式和计算机使用能力，让该模型创建了一个与Codex说话的设备，没有团队成员直接编辑设计产出。 Astra定义了功能要求，开发了附件和内部组件支持，选定了电气组件，并设计了印刷电路板，包括组件布置和连接。 它还设计了用户界面，并产生了将设备与Codex连接起来所需的固件和软件集成。
在较早的一次Astra演示中，该模型将组件和路由铜连接置于基卡德，将电子图版转换为印刷电路板布局。
两名队员在一周内与Astra一起设计了测试装置，同时作为副项目进行工作。 一个合同制造商随后在不到一个月的时间内生产了一小批原型机。
制造业准备状态仍然需要几轮审查和重复。 Astra没有始终考虑到制造商的生产限制、耐受性或装配要求。 正在利用这些缺点来确定未来的模型需要改进哪些，在生成完整的设计和为生产做准备之间作出区分。
国内收养和普通收养
通过机器人和计算基础设施工作，OpenAI的硬件和基础设施团队使用Codex和ChatGPT进行计算机辅助设计模型和修改，制造图纸，模拟设置和设计审查。 OpenAI报道称，其硬件团队的模型使用量以符号衡量，自2月以来增长了23倍。 该图跟踪模型消耗，而不是直接测量工程产出或节省的时间。
BenchCAD 2.0提供了对设计能力的单独评估，要求模型重新创建3D部件和组件，并确定电路板连接。 OpenAI报告较新的内部模型的平均得分为0.875，与之相比，GPT-6 Astra在最高推理设置时的得分为0.594，并提出了收益作为早期证据，表明AI模型可以加速专业迭代，同时注意到设计只是工程生命周期的一部分。
这在基准测试的性能和生产准备状态之间留下了明显的空白：评价涵盖了与设计有关的任务，而设备实验显示如何制造需求仍然需要进一步的审查。
奇普·加纳西拉辛和马兹达的工程评论
OpenAI正与少数工业合作者测试这些能力，他们的工程师审查模型输出的准确性和实用性。 它们的评估旨在确定哪些任务模式可以支持，哪些必须改进，以便安全可靠地部署。
在Chip Ganasi Racing，扩展协作测试工作流程，将现有部件的3D扫描转换成可编辑的CAD模型和2D制造图纸，尤其是在原始设计文件不可用的情况下。 CGR工程师检查所产生的模型和图纸的准确性和可制造性，提供反馈，说明在哪些方面可以减少人工工作，哪些方面需要改进。
在第一年里，奇普·加纳西赛车伙伴关系侧重于从汽车传感器、赛车和坑乘员表演中解释数据。 新的工作流程为早期的数据工作增加了物理部分工程和原型。
马兹达合作将OpenAI的研究和前方部署的工程团队与马兹达空气动力学和坠机安全工程师对齐。 各小组正在利用气流和结构行为模拟来制定和评价车辆设计，同时考虑造型和成本。 在空气动力学工作方面，GPT-6 Astra生成替代鼻子形状，运行气流模拟，并完善有希望的设计，以减少拖曳。
称模拟了数十种变体，帮助工程师了解哪些特征对空气动力学很重要，OpenAI计划分享研究的进一步进展和成果。
OpenAI还邀请参与设计，模拟，制造和生产的工程公司和软件供应商为未来的合作提出高价值用途和工具。 这些项目的中心问题是，更好的设计产出能否通过生产限制和工程审查进行，这些阶段仍然决定一个生成的设计是否成为可制造的产品。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
AI Models & Platforms
OpenAI Tests Astra on Physical Engineering From Devices to Vehicles
Add Unite.AI to your preferred sources on Google
OpenAI announced a set of physical-product engineering projects on November 3, 2026, including an Astra-designed test device, expanded engineering work with Chip Ganassi Racing and a new vehicle-design research collaboration with Mazda.
The capabilities span mechanical and electrical engineering and end-to-end system design, from preparing drawings and setting up simulations to coordinating changes across disciplines. The goal is to give engineers more time to explore ideas and improve designs while connecting work across specialized tools.
An End-to-End Device Experiment
The device experiment tested GPT-6 Astra’s multimodal and computer-use capabilities by having the model create a device for speaking with Codex, without team members directly editing the design outputs. Astra defined the functional requirements, developed the enclosure and internal component supports, selected electrical components and designed the printed circuit board, including component placement and connections. It also designed the user interface and generated the firmware and software integrations needed to connect the device to Codex.
In an earlier Astra demonstration, the model placed components and routed copper connections in KiCad, converting an electronic schematic into a printed-circuit-board layout.
Two team members designed the test device with Astra in under a week while working on it as a side project. A contract manufacturer subsequently produced a small batch of prototypes in under a month.
Manufacturing readiness still required several rounds of review and iteration. Astra did not consistently account for the manufacturer’s production constraints, tolerances or assembly requirements. Those shortcomings are being used to identify what future models need to improve, establishing a distinction between generating a complete design and preparing that design for production.
Internal Adoption and BenchCAD 2.0
Across robotics and compute-infrastructure work, OpenAI’s hardware and infrastructure teams use Codex and ChatGPT for computer-aided design modeling and revisions, manufacturing drawings, simulation setup and design reviews. OpenAI reports that model usage across its hardware teams, measured in tokens, has grown 23-fold since February. That figure tracks model consumption rather than directly measuring engineering output or time saved.
BenchCAD 2.0 provides a separate assessment of design capability, asking models to recreate 3D parts and assemblies and determine circuit-board connections. OpenAI reports an average score of 0.875 for a newer internal model, compared with 0.594 for GPT-6 Astra at its highest reasoning setting, and presents the gain as early evidence that AI models could accelerate professional iteration while noting that design is only one part of the engineering lifecycle.
That leaves a clear gap between benchmark performance and production readiness: the evaluation covers design-related tasks, while the device experiment shows how manufacturing requirements can still necessitate further review.
Engineering Review at Chip Ganassi Racing and Mazda
OpenAI is testing these capabilities with a small group of industry collaborators whose engineers review model outputs for accuracy and practical use. Their assessments are intended to identify which tasks models can support and what must improve for safe, reliable deployment.
At Chip Ganassi Racing, the expanded collaboration tests workflows that convert 3D scans of existing parts into editable CAD models and 2D manufacturing drawings, particularly when the original design files are unavailable. CGR engineers check the resulting models and drawings for accuracy and manufacturability, supplying feedback on where manual work can be reduced and where the models need improvement.
During its first year, the Chip Ganassi Racing partnership concentrated on interpreting data from car sensors, races and pit-crew performance. The new workflows add physical-part engineering and prototyping to that earlier data work.
The Mazda collaboration pairs OpenAI’s research and Forward Deployed Engineering teams with Mazda aerodynamics and crash-safety engineers. The teams are using airflow and structural-behavior simulations to develop and evaluate vehicle designs while accounting for styling and cost. In the aerodynamics work, GPT-6 Astra generates alternative nose shapes, runs airflow simulations and refines promising designs in an effort to reduce drag.
Akihiro Nakata, a principal engineer at Mazda, said simulating dozens of variations helps engineers understand which features matter for aerodynamics and where designers have room to explore. OpenAI plans to share further progress and results from the research.
OpenAI is also inviting engineering firms and software vendors involved in design, simulation, manufacturing and production to propose high-value uses and tooling for future collaborations. The central question across these projects is whether better design outputs can carry through production constraints and engineering review, the stages that still determine whether a generated design becomes a manufacturable product.
Auto-fetched and translated by AI Atlas. All rights belong to the original author.