FMSG: Cyber: Learning Foundation Models for Manufacturing Design Automation

FMSG：网络：制造设计自动化的学习基础模型

• 批准号: 2328032
• 负责人: Qi Zhu
• 金额: $ 50万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2328032&HistoricalAwards=false
• 关键词: FMSG; Cyber; Learning; Foundation; Models

Large foundation models, such as GPT-4, LLaMA, CLIP, and BLIP-2, have demonstrated remarkable intelligence in interpreting user input and generating corresponding content. Such advancements reveal the potential for automating the manufacturing design process, as new foundation models could be created to understand a designer's intentions via inputs such as natural language, sketches, photos, or other modalities. These models can then automatically generate manufacturing designs in the form of CAD (Computer-Aided Design) models, and from which, further help the generation of manufacturing process instructions (e.g., G-code) by optimizing process selections and parameter settings. To realize this vision, this Future CyberManufacturing research project develops novel foundation models and learning methods that enable manufacturing design automation. It addresses unique characteristics and challenges in manufacturing designs, such as specific data types and stringent requirements (e.g., product specifications, manufacturing constraints, material selections), complex and diverse manufacturing processes, and difficulty in collecting large amounts of high-quality training data. The success of the project could bring transformative impacts by reducing design time, minimizing costs, and increasing product diversity and quality. Furthermore, a highly automated manufacturing design process could lower barriers for designers without significant manufacturing expertise, unleash their creativity, and increase labor participation in manufacturing. The end products would be more diverse and better suited to customer needs, thus benefiting society as a whole. The project develops a two-stage framework that includes novel foundation models and learning methods for manufacturing design automation. The first stage takes natural language and possibly additional images (drawings, sketches, photos, etc.) as input, and generates CAD models in textual representation as output, possibly followed by a manual model validation and revision step. This automatic generation of CAD models are enabled by novel methods for manufacturing-driven tuning of existing pre-trained language and vision models, multi-modal model fusion in manufacturing-specific representation space, design of a CAD generative decoder, and prompt engineering for model improvement. The second stage takes CAD models as input, along with optional textual hints (e.g., preferred manufacturing processes, cost constraints, etc.), and generates optimized manufacturing decisions, particularly the selection of processes and the setting of key parameters. These decisions, combined with existing tools, can help generate detailed manufacturing process instructions (e.g., G-code, additive manufacturing instructions). This stage includes novel methods for unsupervised learning of a process-agnostic foundation language model, supervised multi-task learning of process-dependent backends for optimizing process parameters, and reinforcement learning for process selection and further improvement of the input CAD model.This Future Manufacturing research is supported by the Computer and Information Science and Engineering Directorate's Division of Computer and Network Systems (CISE/CNS) and the Social, Behavioral and Economic Sciences Directorate’s Division of Social and Economic Sciences (SBE/SES).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

GPT-4，Llama，Clip和Blip-2等大型基础模型在解释用户输入和生成相应的内容方面表现出了显着的智能。这些进步揭示了自动化制造设计过程的潜力，因为可以创建新的基础模型，以通过自然语言，草图，照片或其他方式来理解设计师的意图。然后，这些模型可以自动以CAD（计算机辅助设计）模型的形式生成制造设计，并通过优化过程选择和参数设置来进一步帮助生成制造过程指令（例如G-Code）。为了实现这一愿景，这一未来的网络制造研究项目发展新颖的基础模型和学习方法，使制造设计自动化。它解决了制造设计中的独特特征和挑战，例如特定的数据类型和严格的要求（例如，产品规格，制造限制，材料选择），复杂而多样化的制造过程，在收集大量高质量培训数据方面很难。该项目的成功可以通过减少设计时间，最小化成本以及提高产品多样性和质量来带来变革性的影响。此外，除非其创造力并增加制造业的劳动力参与，否则高度自动化的制造设计过程可以降低设计人员的障碍。最终产品将更加多样化，更适合客户需求，从而使整个社会受益。该项目开发了一个两阶段的框架，其中包括用于制造设计自动化的新颖基础模型和学习方法。第一阶段将自然语言和可能的其他图像（图，草图，照片等）作为输入，并以文本表示形式生成CAD模型作为输出，然后进行手动模型验证和修订步骤。这种自动生成的CAD模型是通过用于制造现有的预训练语言和视觉模型的新方法来启用的，在制造特异性表示空间中的多模式模型融合，CAD通用解码器的设计以及及时的工程工程进行模型改进。第二阶段将CAD模型作为输入，以及可选的文本提示（例如，首选的制造过程，成本限制等），并生成优化的制造决策，尤其是过程的选择和关键参数的设置。这些决定及其现有工具可以帮助生成详细的制造过程指令（例如G代码，增材制造说明）。 This stage includes novel methods for unsupervised learning of a process-agnostic foundation language model, supervised multi-task learning of process-dependent backends for optimizing process parameters, and reinforcement learning for process selection and further improvement of the input CAD model.This Future Manufacturing research is supported by the Computer and Information Science and Engineering Directorate's Division of Computer and Network Systems (CISE/CNS) and the Social, Behavioral and Economic Sc​​iences Directorate's Division社会和经济科学（SBE/SES）。该奖项反映了NSF的法定使命，并通过使用基金会的知识分子优点和更广泛的影响标准来评估，以诚实的支持。