We take the C-type chip, spiral chip and random-curl chip commonly produced in automated turn processing as the objects of study to inspect the angle of chip flow by using image recognition, the results of test shows that the changing scale of angle of C-type chip and spiral chip flow is 2 degrees and the changing scale of random curl chip flow is 8 degrees.

Automated inspection of chip type using image analysis method

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%.

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Methods of recognizing chip shape based on neural net

According to the profile characteristics of the extracted chip form and the number of nodes between profile and * line which have the same gravity, chip species are discerned. The chip types near the critical joint are judged exactly through the standard tolerance of all direction nodes. It was tested to have a high accordant rate.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An Meng

Papers

Image-based recognition of the chip shape