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首页> 外文期刊>Journal of Intelligent Manufacturing >Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties
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Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

机译：监控刀具磨损并选择最佳切削条件，并具有渐进的刀具磨损效果和输入不确定性

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摘要

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.
机译：加工中的一大挑战是在正确的时间更换切削刀具。用钝的工具进行加工可能会降低产品质量。但是，如果切削工具仍然能够继续进行切削操作，则可能无需更换切削工具。这两种情况都会增加生产成本。因此，有效的工具状态监视系统可以降低生产成本并提高生产率。本文提出了一种基于神经网络的传感器融合模型，用于车削操作中的刀具磨损监测系统。小波包树方法用于分析所采集的信号，即切削刀架中的切削应变和电机电流，以及提取磨损敏感特征。一旦提取了可能的特征列表，就可以使用主成分分析来减少输入特征空间的尺寸。提出并验证了新颖的策略，例如已开发的ANN模型针对输入数据中不确定性的鲁棒性，以及将监视信息集成到优化系统中，以便利用渐进式刀具磨损信息来选择最佳切削条件。在手动车削操作中。该方法既简单又灵活，足以用于在线实施。

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