Aiming at the problem of process monitoring on chip generating in automatic machining, methods of recognizing chips' shape based on neural net are researched in this paper. The conception of area ratio of the chip image to the located window is defined, the area ratio feature has been proposed because the size of all windows and the direction of chips are respectively same. At the same time, the Euler number characteristic and disperse degree characteristic of the chip image have been worked out. The above geometry characteristics of the chip image are chosen as input vectors of neural network, and the 50 various images of each type such as C shape, spiral shape and disorderly shape are chosen as training sample, the recursion least square law is used to train network. The recognition rate and training time of the BP network are compared with those of the RBF network, so the conclusion that the RBF network is superior to the BP network at the aspect of chip shape recognition has got, and the relevant computer program has been developed, which possess good real-time application and adaptability by way of the experiment certification. The recognition rate achieves more than 90%.
As monitoring the abrasion of tools becomes more and more important during metal cutting, many research efforts have been made in this aspect. This paper proposed a new method that detect and measure it directly by digital image technology and developed a detecting and measure system based on digital image. It is prone to automatically disposed and to integrate with machining and control information.
A new filter method that average many same size images when acquiring images is proposed. At the same time, a new edge detection way by wavelet transform is also proposed. A new wavelet function is given when selection wavelet functions, which describe gray change of images more availably, as well as may avoid the jamming of noise. The rule of three is proposed to calibrate this system in this paper, and calibration precision arrives at application requirement. Functions of the software of this system include acquiring and processing images, edge detecting and segmenting images, and measure and analysis images. Images of tools are acquired by camera and CCD and video card. It can automatically differentiate and pick-up available information of the original image, such as area and perimeter and width and length and the location of the center of the abrasion region, by image processing and segmenting.
Since chip shape directly affects the normal running ofthe automatic machining system, such as CNC, FMS, CIMS and so on In order to ensure their normal running, the supervision of tool cutting state was necessary and significative research task. In this paper, we analyze the method ofindirectly supervising chip shape by signal changing, for example cutting force, cutting temperature, Acoustic Emission, ray-electronic signal and sound signal when chip effuses out etc. and point out their flaw in theory or application and bring forward chip recognizing method based on chip shape features. The differentiate method according to gray-scale of chip image, can get chip shape feature what need by setting appropriate value. Then the feature is extended and transformed and get new shape feature. Finally recognize chip shape by calculating euler number of new feature. This paper puts forward recognizing theory's algorithm and develops computer program. The method can recognize typical cutting chip, such as "C" chip, spiral chip and abnormal chip and was proved to have a good precision by recognizing chip experiment in practical machining.
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