NSF Young Investigator: Material Processing and Intelligent Manufacturing

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、现场产品质量监控和实时过程控制。该计划最初将专注于模压和先进的复合材料产品。将解决两个基本的制造科学问题，即(1)大多数制造过程没有被完全理解，(2)在制造过程中没有对产品质量进行监控和优化调整。改进的成型和先进复合材料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工艺实现最佳产品质量。要做到这一点，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。将制定业务方法。将研究几种感觉传感器机制，最初的重点是嵌入的光学和电子纤维。将通过开发结合神经网络的基于知识的工具来实现实时过程适应。这项工作的一个新组成部分将是从过程模拟模型而不是从实际制造数据采购神经网络训练信息。总体而言，协同制造方法的开发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。

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.