Bearing is a basic work-piece in machinery devices, and surface quality of steel ball is the main factor which affects the
precision and longevity of bearing. Currently defects of steel ball are detected manually in industry. It is inefficiency and
of high probability of misidentification. In order to assure the stability of steel ball quality this paper put forward an autodetection method based on vision technique to detect surface defects of steel ball. Firstly we designed an approach to
fully expand the surface of steel ball according to the requirement of image detection. Then we made up a corresponding
device to accomplish to designed approach and developed a platform system for image detection. Finally we carried a
proving detection in some kind of defects of steel ball. The result of test shows that the method can be put into use to
detect the general defects detection of steel ball.
In the evaluation of surface roughness by computer vision technique, the pattern of illumination is generally correlated
with optical surface finish parameters from the images. So this paper carried out experiments to investigate the effects of
various factors and completed the optimum design of capture condition. Then we captured abundant sample images
under appropriate experimental condition and chose to extract features of surface roughness in the spatial frequency
domain which should be less sensitive to noise than spatial domain features. Therefore, artificial neural network (ANN),
which took frequency-domain roughness features as the input, was developed to determine surface roughness by
selecting the back-propagation algorithm. The built ANNs using these critical sets of inputs showed low deviation from
the training data, low deviation from the testing data and high sensibility to the inputs levels. And the high prediction
accuracy of the developed ANNs was confirmed by the good agreement with the results from traditional stylus method.
Hence the proposed roughness features and neural network were efficient and effective for automated assessment of
surface roughness.
This paper observed the chip forming and effusing process when high speed hard turning hardened steel using PCBN
tools under two-dimensional longitudinal turning and transverse turning by high-speed photography, and obtained the
chip formation and efflux states with different cutting edge preparation and parameters. The experiment results showed
that the sharp-edged tool was useful for chip forming, but strength of its edge is low and the tool life is short, and that the
tool has longer life under the chamfered edge, but too small or too large cutting thickness goes against the chip forming
and effusing.
High efficiency and high automation are essential in the process of metal cutting. How to control the chip will affect processing quality, cutting tool life and productivity greatly. With the development of image processing technology, machine vision has been widely used in real-time monitoring of chip shape. A set of machine vision detection system is developed for realizing image capture, image processing, image pattern matching and image analysis in real time in this paper. Especially, dynamic template is designed to match the complex chip. In this system, LabVIEW is used as system platform, QP 300 picture capture card of Daheng-Image cooperation is used as image capture hardware, LED of CCS cooperation is used as light source. The actual operation shows that this system can identify typical C shape chip and spiral shape chip. Meanwhile, other functions are developed, such as parameter optimization and network transmission.
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%.
Along with the fast development of image technology, its application has nearly reached every field. Image measurement technology has larger measurement speed and its results are prone to observe, contrast, analyze and save. In this study, an image measurement system composed of a CCD camera, a computer and a LED lamp-house, was used to study the characteristic of electron images of machined surfaces obtained under different processing methods. The corresponding relations between the character of different surface textures and of different electron images were established. The character of the machined surfaces' images with different cutting parameters was also studied and the image character parameters' range of standard roughness was established. Meanwhile, image measurement methods of the machined surfaces' quality were studied. Image methods and evaluation indicator in this study have good application performance, which were established by comparing them with traditional measurement methods' results.
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.
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.
This paper researches two shapes of the tool face damage, i.e. damage and crater wear, using image analysis method. The principle of detecting them is presented; the judging standards of tool damage are given; two experiments are cited.
KEYWORDS: Inspection, Image processing, Image analysis, Signal processing, Fermium, Frequency modulation, Analytical research, Cameras, Lithium, Control systems
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.
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