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
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.