The defect inspection in traditional metal processing usually adopts the manual visual inspection, which is easy to cause problems such as false detection and leakage. Besides, the detection rate of existing metal defect inspection has been affected by the high reflectivity of metal surface. For avoiding the problems of existing detection algorithms, a metal defect detection system based on laser triangulation and laser vision is given. The system is composed of industrial camera and strip light source. In order to avoid the interference of metal reflection on defect detection, the green strip light is used as the light source of the system. Based on the proposed system, this paper presents a metal surface detection algorithm with three primary color channels, that is, metal surface defects are identified by identifying the RGB values of metal surface pixels. Finally, through static experiments, the detection rate of the metal defect surface detection system based on laser vision is 100%. The defect detection method proposed in this paper can effectively avoid the influence of high reflectivity of metal surface on the detection rate, and can be widely used in the field of metal processing.
An irradiance distribution model for laser triangulation displacement sensors is proposed, which is more suitable for practical applications than the geometric optical model (GOM). Based on the irradiance distribution model, a convergence algorithm is developed. Compared with the design parameter optimized method based on the GOM, the convergence algorithm can be used for optimization of not only the traditional design parameters including view angle and object distance but also the waist of the source laser beam. Additionally, using this convergence algorithm, the nonlinearity can be evaluated in the design stage. To validate the convergence algorithm, two example cases are performed. The results show that the nonlinearities are reduced to 6.3098 and 5.7946 μm, respectively.
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