ANN assisted sensor fusion model to predict tool wear during hard turning with minimal fluid application
ANN assisted sensor fusion model to predict tool wear during hard turning with minimal fluid application
复制标题
人工神经网络辅助传感器融合模型可预测硬车削过程中的刀具磨损，并使用最少的流体
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
A. Varadarajan
中科院分区：
文献类型：
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作者：
P. Paul;A. Varadarajan
如果能有效地综合利用切削力、切削温度、声发射信号和振动信号等因素，就能准确地预测刀具磨损。这些因素中的每一个都以其特有的方式预测刀具磨损-高切削温度是后刀面磨损和弧坑磨损的指标，而切削力的变化更有效地指示刀具失效的断裂类型。尽管这些因素中的每一个都可以单独使用，但通过共同考虑刀具磨损指数而不是单独考虑刀具磨损指数，可以进行更准确的预测。在目前的工作中，试图融合切削力，切削温度和位移的刀具振动沿着与切削速度，进给量和切削深度，以预测刀具磨损过程中的AISI 4340钢的46 HRC与最小的流体应用与硬质合金刀片雕刻前刀面。建立了回归和神经网络模型，融合切削力、切削温度和刀具振动位移信号预测刀具后刀面磨损。从结果中，据观察，基于人工神经网络的模型被发现是上级的回归模型在其预测刀具磨损的能力。
Accurate prediction of tool wear can be made possible if factors like cutting force, cutting temperature, acoustic emission signals and vibration signals are used effectively and collectively. Each of these factors predicts tool wear in their own characteristic fashion – high cutting temperature is an index of flank wear and crater wear, whereas variation in cutting force indicates fracture type of tool failure more effectively. Even though each of these factors can be used individually, a more accurate prediction will be possible by considering the indices of tool wear collectively rather than individually. In the present work, an attempt was made to fuse cutting force, cutting temperature and displacement of tool vibration along with cutting velocity, feed and depth of cut to predict tool wear during turning of AISI 4340 steel of 46 HRC with minimal fluid application using hard metal insert with sculptured rake face. A regression and an ANN model were developed to fuse the cutting force, cutting temperature and displacement of tool vibration signals to predict tool flank wear. From the results, it was observed that the model based on ANN was found to be superior to the regression model in its ability to predict tool wear.