CAREER: Neural Network Strategy for Machining When Data is Sparse

Twomey人工神经网络（ANN）需要大量的观测值来保证良好的泛化，也需要大量的独立观测值（或数据）来评估网络的泛化性能。人工神经网络可能是监测和控制制造过程的真正智能系统中最重要的数据处理技术之一。然而，制造商通常不愿意（如果不是抵制的话）使用人工神经网络方法，因为相关的用户专业知识成本、较长的开发时间以及对大量训练数据的需求。对评价方法的研究很少，在数据稀疏的情况下，训练和评价并行的问题被忽视。本CAREER项目的研究目标是为数据稀疏的制造情况开发一种人工神经网络训练和评估策略。项目中的方法寻求同时使用稀疏数据进行网络训练和评估。本研究结果将应用于两种制造工艺：钻孔和电化学加工。利用人工神经网络和其他信息处理技术将发展成为本科课程的课程。该研究将通过在数据稀疏的情况下开发人工神经网络训练和验证策略来增加人工神经网络的效用。该计划连同资讯处理课程的发展，将为吸引有才能的学生从事研究工作提供途径，并将体现鼓励妇女和少数族裔视自己为明日的工程师的做法。

DMI-9733747 Twomey Artificial Neural Networks (ANN) require large numbers of observations to ensure good generalization, and a large number of independent observations (or data) to evaluate the networks generalization performance. ANN are potentially one of the most important data processing technologies in a truly intelligent system for the monitoring and control of manufacturing processes. However, manufacturers are often reluctant, if not resistant, to use an ANN approach because of the associated costs of user expertise, long development times, and the need for large amounts of training data. Very little research has been performed on evaluation methods, and the problems of training and evaluation concurrently, when data is sparse, have been overlooked. The research objective of this CAREER project is to develop an ANN training and evaluation strategy for manufacturing situations where data is sparse. The approach in the project seeks the simultaneous use of sparse data for both network training and evaluation. The results of this research will be applied to two manufacturing processes: drilling and electrochemical machining. The utilization of ANN and other information processing technologies will be developed into courses for the undergraduate curriculum. The research will increase the utility of ANN through the development of an ANN training and validation strategy in cases where data is sparse. Together with the development of curriculum in information processing, the project will provide a means for attracting talented students to careers in research, and will embody practices that encourage women and ethnic minorities to see themselves as tomorrow's engineers.