High-speed measurement algorithm for the position of holes in a large plane

CMM is widely used to measure the position of the holes on the top surface of an engine block. However, CMM requires a perfect environment and cannot be applied in online measurements. Moreover, using CMM to measure the position of holes in a large plane takes more than 10min, thus lowering its efficiency. To solve this problem, this paper presents a high-speed measurement algorithm for the position of holes in a large plane based on a flexible datum and the feature neighborhood model. First, two area CCD cameras that grab the images of the reference holes of the block are used to establish the flexible datum. Second, different mapping models are built in the neighborhood of the center of different holes. These black-box mapping models ignore the intermediate process of camera perspective projection. Finally, the feature points in the different scales of the neighborhood of the hole centers solve the mapping results. The mapping results are then weighted by using the multi-scale weighting algorithm. The calibration target is designed, but the feature points in the target are minimal. Thus, a new method to create several more feature points is designed. Compared with the measurement result of CMM, the maximum position error of the measurement system is 0.025mm. The relative error is better than 0.025% and the standard deviation of the measurement data is less than 0.010mm. With a conference level of 95%, the system measurement uncertainty is better than ±0.020mm. The measuring time is less than 3min. The position measurement scheme features high automation and high efficiency, and can be used in the position measurement of online engine block holes. ► High-speed algorithm of holes position based on vision flexible datum is proposed. ► Mapping model is built in each neighborhood of holes center to decrease the error. ► New 2D mapping model is proposed, which is called the black-box model. ► The model based on multi-scale weighting achieves the unity of the model feature.