Efficient tool condition monitoring techniques help to realize intelligent management of tool life and reduce tool usage costs.In this paper,the influence of different wear degrees of ball-end milling cutters on the t...Efficient tool condition monitoring techniques help to realize intelligent management of tool life and reduce tool usage costs.In this paper,the influence of different wear degrees of ball-end milling cutters on the texture shape of machining tool marks is investigated,and a method is proposed for predicting the wear state(including the position and degree of tool wear)of ball-end milling cutters based on entropy measurement of tool mark texture images.Firstly,data samples are prepared through wear experiments,and the change law of the tool mark texture shape with the tool wear state is analyzed.Then,a two-dimensional sample entropy algorithm is developed to quantify the texture morphology.Finally,the processing parameters and tool attitude are integrated into the prediction process to predict the wear value and wear position of the ball end milling cutter.After testing,the correlation between the predicted value and the standard value of the proposed tool condition monitoring method reaches 95.32%,and the accuracy reaches 82.73%,indicating that the proposed method meets the requirement of tool condition monitoring.展开更多
The Mahalanobis distance features proposed by P.C.Mahalanobis, an Indian statistician, can be used in an automatic on-line cutting tool condition monitoring process based on digital image processing. In this paper, a ...The Mahalanobis distance features proposed by P.C.Mahalanobis, an Indian statistician, can be used in an automatic on-line cutting tool condition monitoring process based on digital image processing. In this paper, a new method of obtaining Mahalanobis distance features from a tool image is proposed. The key of calculating Mahalanobis distance is appropriately dividing the object into several component sets. Firstly, a technique is proposed that can automatically divide the component groups for calculating Mahalanobis distance based on the gray level of wearing or breakage regions in a tool image. The wearing region can be divided into high gray level component group and the tool-blade into low one. Then, the relation between Mahalanobis distance features of component groups and tool conditions is investigated. The results indicate that the high brightness region on the flank surface of the turning tool will change with its abrasion change and if the tool is heavily abraded, the area of high brightness will increase apparently. The Mahalanobis distance features of high gray level component group are related with wearing state of tool and low gray level component group correlated with breakage of tool. The experimental results show that the abrasion of the tool’s flank surface affected the Mahalanobis distances of high brightness component of the tool and the pixels of high brightness component set. Compared with the changes of them, we found that the Mahalanobis distance of high brightness component of the tool was more sensitive to the abrasion of cutting tool than the area of high brightness component set of the tool. Here we found that the relative changing rate of the area of high brightness component set was not quite obvious and it was ranging from 2% to 15%, while the relative changing rate of the Mahalanobis distance in table 1 ranges from 13.9% to 47%. It is 3 times higher than the changing rate of the area.展开更多
基金Project(51975169)supported by the National Natural Science Foundation of ChinaProject(LH2022E085)supported by the Natural Science Foundation of Heilongjiang Province,China。
文摘Efficient tool condition monitoring techniques help to realize intelligent management of tool life and reduce tool usage costs.In this paper,the influence of different wear degrees of ball-end milling cutters on the texture shape of machining tool marks is investigated,and a method is proposed for predicting the wear state(including the position and degree of tool wear)of ball-end milling cutters based on entropy measurement of tool mark texture images.Firstly,data samples are prepared through wear experiments,and the change law of the tool mark texture shape with the tool wear state is analyzed.Then,a two-dimensional sample entropy algorithm is developed to quantify the texture morphology.Finally,the processing parameters and tool attitude are integrated into the prediction process to predict the wear value and wear position of the ball end milling cutter.After testing,the correlation between the predicted value and the standard value of the proposed tool condition monitoring method reaches 95.32%,and the accuracy reaches 82.73%,indicating that the proposed method meets the requirement of tool condition monitoring.
文摘The Mahalanobis distance features proposed by P.C.Mahalanobis, an Indian statistician, can be used in an automatic on-line cutting tool condition monitoring process based on digital image processing. In this paper, a new method of obtaining Mahalanobis distance features from a tool image is proposed. The key of calculating Mahalanobis distance is appropriately dividing the object into several component sets. Firstly, a technique is proposed that can automatically divide the component groups for calculating Mahalanobis distance based on the gray level of wearing or breakage regions in a tool image. The wearing region can be divided into high gray level component group and the tool-blade into low one. Then, the relation between Mahalanobis distance features of component groups and tool conditions is investigated. The results indicate that the high brightness region on the flank surface of the turning tool will change with its abrasion change and if the tool is heavily abraded, the area of high brightness will increase apparently. The Mahalanobis distance features of high gray level component group are related with wearing state of tool and low gray level component group correlated with breakage of tool. The experimental results show that the abrasion of the tool’s flank surface affected the Mahalanobis distances of high brightness component of the tool and the pixels of high brightness component set. Compared with the changes of them, we found that the Mahalanobis distance of high brightness component of the tool was more sensitive to the abrasion of cutting tool than the area of high brightness component set of the tool. Here we found that the relative changing rate of the area of high brightness component set was not quite obvious and it was ranging from 2% to 15%, while the relative changing rate of the Mahalanobis distance in table 1 ranges from 13.9% to 47%. It is 3 times higher than the changing rate of the area.
