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并演示一套用于端面铣削加工质量保证的智能加工监测与诊断系统。这将基于多个传感器输出的集成，例如声发射、振动、切削力和切割温度，通过与专家系统相结合的人工神经网络来集成切割条件。产品质量特性，如表面光洁度和公差，将被评估，并最终由系统在端面铣削过程中保持。研究团队将进行机械加工实验，以获取数据并确定与产品质量相关的最佳传感器特征，开发用于估计产品质量的多传感器集成神经网络，开发用于识别趋势的趋势分类神经网络，并对这些网络的性能进行培训和评估。制造业的产品质量至关重要，特别是考虑到我们在全球化市场中面临的激烈竞争。这种性质的开创性工作，将人工智能的最新发展应用于制造业的基本问题，并与一家美国机床制造商密切合作，将有助于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.