Research Initiation: Intelligent Machining Monitoring and Diagnostic System for Quality Assurance Based on Multiple Sensor Integration

本研究的主要目的是开发和 展示智能加工监控和诊断 端铣质量保证系统。 这将基于 多个传感器输出的集成，如声学 排放、振动、切削力和切削温度， 基于人工神经网络的切削条件 专家系统。 产品质量特性，如 将估计表面光洁度和公差，因此， 最终在端铣期间由系统保持。的 研究小组将进行加工实验，以获取数据 并确定与产品相关的最佳传感器特征 质量，开发多传感器集成神经网络， 评估产品质量，开发趋势分类神经网络 网络，以确定趋势，培训和评估 这些网络的性能。 产品质量在制造业中至关重要 特别是考虑到我们在 全球化的市场。 这种性质的开创性工作， 人工智能的最新发展应用于 制造业的基本问题，并与一个 美国机床制造商，将帮助国内 制造业成功竞争。

The main objective of this research is to develop and demonstrate an intelligent machining monitoring and diagnostic system for quality assurance in end-milling. This will be based on the integration of multiple sensors outputs, such as acoustic emission, vibration, cutting force, and cutting temperature, with cutting conditions via artificial neural networks in conjunction with an expert system. Product quality characteristics such as surface finish and tolerance will be estimated and, therefore, eventually maintained by the system during end-milling. The research team will conduct machining experiments to acquire data and determine the best sensor feature that correlates to product quality, develop multi-sensor integrated neural networks for estimating product quality, develop trend classification neural network for identifying trends and train and evaluate the performance of these networks. Product quality in manufacturing is of paramount importance especially in view of the tremendous competition that we face in the globalized marketplace. Pioneering work of this nature, where the latest developments in artificial intelligence are applied to basic problems in manufacturing and in close cooperation with a United States machine tool manufacturer, will help domestic manufacturing compete successfully.