NSF Young Investigator: Material Processing and Intelligent Manufacturing

NSF青年研究员：材料加工与智能制造

• 批准号: 9258026
• 负责人: John Coulter
• 金额: $ 6.25万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-09-01 至 1994-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9258026&HistoricalAwards=false
• 关键词: NSF; Young; Investigator; Material; Processing

Material processing and intelligent manufacturing will be studied in an interdisciplinary format involving process modeling, in-situ product quality monitoring, and real-time process control. The program will initially focus on molded and advanced composite products. Two fundamental manufacturing science problems will be addressed, which are (1) the majority of manufacturing processes are not completely understood, and (2) product quality is not monitored and optimally adjusted during fabrication. Improved process models for molding and advanced composites will then be employed in real-time environments to direct manufacturing processes toward optimal product quality in a feedback control manner. Doing so will require the development of sensor subsystems for in-situ product quality assessment and knowledge based tools for real-time manufacturing precess adaption. Methodologies for operations will be developed. Several sensory transducer mechanisms will be investigated, with an initial focus on embedded optical and electronic fibers. Real-time process adaptation will be accomplished through the development of knowledge based tools incorporating neural networks. A novel component of the effort will be the procurement of neural network training information from process simulation models rather than from actual manufacturing data. Overall, the synergistic manufacturing methodology development will decrease both process development time and cost while improving product quality and process flexibility.

材料加工和智能制造将以跨学科的形式进行研究，包括过程建模，现场产品质量监测和实时过程控制。该项目最初将专注于模制和先进复合材料产品。两个基本的制造科学问题将被解决，这是(1)大多数制造过程不完全理解，(2)产品质量在制造过程中没有被监控和优化调整。改进的成型和先进复合材料的工艺模型将在实时环境中使用，以反馈控制的方式指导制造过程实现最佳产品质量。这样做将需要开发用于现场产品质量评估的传感器子系统和用于实时制造过程适应的基于知识的工具。将制订行动方法。将研究几种传感传感器机制，最初的重点是嵌入式光学和电子纤维。实时过程适应将通过结合神经网络的基于知识的工具的开发来完成。这项工作的一个新组成部分将是从过程仿真模型中获取神经网络训练信息，而不是从实际制造数据中获取。总体而言，协同制造方法的发展将减少工艺开发时间和成本，同时提高产品质量和工艺灵活性。