Geometric Modeling and Artificial Intelligence for Design and Manufacturing Automation

用于设计和制造自动化的几何建模和人工智能

• 批准号: 9214996
• 负责人: Aristides A. Requicha
• 金额: $ 19.51万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-01-01 至 1996-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9214996&HistoricalAwards=false
• 关键词: Geometric; Modeling; Artificial; Intelligence; Design

This project is centered on three-dimensional spatial reasoning and its applications in intelligent engineering systems. Theories and techniques from the fields of geometric modeling and artificial intelligence (AI) are integrated so as to attack difficult problems that arise in design, manufacturing and other activities in the life cycle of mechanical and electromechanical products. The main body of the research is a natural sequel to previous, National Science Foundation supported work on automatic recognition of machinable features, and on accessibility analysis for the inspection of mechanical parts with Coordinate Measuring Machines (CMMs). USC's feature recognizer will be integrated downstream, with experimental process planners and with commercial NC (Numerical Control) cutter-path generators for driving machine tools. It will also be integrated upstream, with feature-based design systems. The existing feature finder is very promising because it can handle complex volumetric interactions between features, and produces removal volumes that satisfy stringent conditions for machinability. It will be redesigned so as to operate incrementally, thereby providing immediate manufacturing feedback to a designer. It will also be extended so as to extract the geometric information required by process planners and NC code generators. Research on inspection planning will seek to produce, fully automatically, complete programs for driving CMMs (and potentially other equipment such as laser probes), given toleranced solid models of mechanical parts. Dimensional inspection is closely related to tolerancing, and will build upon on-going work at USC on the semantics of tolerance specifications. Issues of measurement interpretation (soft gaging) also will be addressed.

该项目以三维空间为中心， 推理及其在智能工程系统中的应用。 几何造型和几何造型领域的理论和技术 人工智能（AI）被集成，以便攻击 在设计、制造和其他方面出现的难题 机械和机电产品生命周期中的活动 产品. 研究的主体是以前的自然续集， 美国国家科学基金会支持的自动识别工作 的可加工特征，以及 用三坐标测量机检验机械零件 （CMM）。南加州大学的特征识别器将被整合到下游， 与实验过程规划和商业数控 用于驱动机的（数控）刀具轨迹生成器 工具. 它还将被集成到上游， 设计系统 现有的特征查找器非常有前途 因为它可以处理复杂的体积之间的相互作用 功能，并产生去除量，满足严格的 可加工性的条件。 它将被重新设计，以便 逐步操作，从而提供即时制造 反馈给设计师。 它也将被延长，以提取 工艺规划人员和NC代码所需的几何信息 发生器. 关于视察规划的研究将力求产生， 全自动，完整的程序驱动坐标测量机（和 可能是其他设备，例如激光探针）， 机械零件的实体模型。 尺寸检查 与公差密切相关，并将建立在正在进行的工作 在南加州大学的语义公差规格。 问题 测量解释（软测量）也将得到解决。