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首页> 外文期刊>Journal of Manufacturing Processes >A multimodal intelligent monitoring system for turning processes
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A multimodal intelligent monitoring system for turning processes

机译：用于车削过程的多模式智能监控系统

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摘要

Online process condition monitoring is an essential component of closed-loop process-level automation of machining operations. This paper describes the development of an intelligent monitoring system for turning processes, which consists of three units: a tool wear predictor, a chatter detector and a tool chipping detector. Features are extracted from the signals of multiple low-cost and low-intrusive sensors, and then normalized using a novel scheme to eliminate their dependence on cutting conditions, workpiece materials and cutting tools. A systematic feature selection procedure, coupled with automated signal preprocessing parameter selection, is presented to select the optimal feature set for each unit. The tool wear unit is built with type-2 fuzzy basis function networks to predict tool wear with uncertainty bounds, while the chatter unit and tool chipping unit are built with support vector machines to maximize the classification fidelity. Experimental results show that the monitoring system achieved high accuracy, generalized applicability and satisfactory robustness for all the three process conditions, by using two affordable sensors: a power meter and an accelerometer. The three monitoring schemes are integrated into a monitoring software so that they can be implemented in different environments with minimal calibration efforts.
机译：在线过程状态监视是加工操作的闭环过程级自动化的重要组成部分。本文介绍了用于车削过程的智能监控系统的开发，该系统由三个单元组成：刀具磨损预测器，颤动检测器和刀具切屑检测器。从多个低成本和低侵入性传感器的信号中提取特征，然后使用一种新颖的方案对其进行归一化，以消除它们对切削条件，工件材料和切削工具的依赖性。提出了系统的特征选择过程，以及自动的信号预处理参数选择，以选择每个单元的最佳特征集。刀具磨损单元由2型模糊基函数网络构建，以预测不确定性范围内的刀具磨损，而颤振单元和刀具削屑单元则由支持向量机构建，以最大程度地提高分类保真度。实验结果表明，通过使用两个价格适中的传感器：功率计和加速度计，该监控系统在所有三个过程条件下均实现了高精度，通用性和令人满意的鲁棒性。这三种监视方案已集成到监视软件中，因此可以以最少的校准工作在不同的环境中实现它们。

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