Aiming at the shortcomings of traditional contact measurement methods such as low measurement efficiency,high cost and low accuracy,a non-contact optical measurement method based on the laser displacement sensor is pr...Aiming at the shortcomings of traditional contact measurement methods such as low measurement efficiency,high cost and low accuracy,a non-contact optical measurement method based on the laser displacement sensor is proposed.According to the relevant regulations of the coaxiality error evaluation standard and the structural characteristics of the compound gear shaft,we have designed and built a set of supporting software system as well as a hardware test platform.In this paper,the distance difference threshold and scale threshold methods are used to eliminate outlier data.The least squares circle is selected to calculate the center of the circle and the minimum containment cylinder axis method is used as the reference axis of the composite gear shaft.Compensated by the standard step shaft calibration,the coaxiality error of the composite gear shaft can be measured to be within 0.01 mm in less than two minutes.The range value of the multi-section measurement test is 0.065 mm.The average coaxiality error is∅0.476 mm.展开更多
An on-machine measuring(OMM)system with a laser displacement sensor(LDS)is designed for measuring free-form surfaces of hypersonic aircraft’s radomes.To improve the measurement accuracy of the OMM system,a novel Iter...An on-machine measuring(OMM)system with a laser displacement sensor(LDS)is designed for measuring free-form surfaces of hypersonic aircraft’s radomes.To improve the measurement accuracy of the OMM system,a novel Iteratively Automatic machine learning Boosted hand-eye Calibration(IABC)method is proposed.Both the hand-eye relationship and LDS measurement errors can be calibrated in one calibration process without any hardware changes via IABC.Firstly,a new objective function is derived,containing analytical parameters of the handeye relationship and LDS errors.Then,a hybrid calibration model composed of two kernels is proposed to solve the objective function.One kernel is the analytical kernel designed for solving analytical parameters.Another kernel is the automatic machine learning(AutoML)kernel designed to model LDS errors.The two kernels are connected with stepwise iterations to find the best calibration results.Compared with traditional methods,hand-eye experiments show that IABC reduces the calibration RMSE by about 50%.Verification experiments show that IABC reduces the measurement deviations by about 25%-50%and RMSEs within 40%.Even when the training data are obviously less than the test data,IABC performs well.Experiments demonstrate that IABC is more accurate than traditional hand-eye methods.展开更多
基金supported by the National Natural Science Foundation of China(No.51975293)Aeronautical Science Foundation of China (No. 2019ZD052010)
文摘Aiming at the shortcomings of traditional contact measurement methods such as low measurement efficiency,high cost and low accuracy,a non-contact optical measurement method based on the laser displacement sensor is proposed.According to the relevant regulations of the coaxiality error evaluation standard and the structural characteristics of the compound gear shaft,we have designed and built a set of supporting software system as well as a hardware test platform.In this paper,the distance difference threshold and scale threshold methods are used to eliminate outlier data.The least squares circle is selected to calculate the center of the circle and the minimum containment cylinder axis method is used as the reference axis of the composite gear shaft.Compensated by the standard step shaft calibration,the coaxiality error of the composite gear shaft can be measured to be within 0.01 mm in less than two minutes.The range value of the multi-section measurement test is 0.065 mm.The average coaxiality error is∅0.476 mm.
基金supported by the National Natural Science Foundation of China (Nos. 51875406 and 51805365)
文摘An on-machine measuring(OMM)system with a laser displacement sensor(LDS)is designed for measuring free-form surfaces of hypersonic aircraft’s radomes.To improve the measurement accuracy of the OMM system,a novel Iteratively Automatic machine learning Boosted hand-eye Calibration(IABC)method is proposed.Both the hand-eye relationship and LDS measurement errors can be calibrated in one calibration process without any hardware changes via IABC.Firstly,a new objective function is derived,containing analytical parameters of the handeye relationship and LDS errors.Then,a hybrid calibration model composed of two kernels is proposed to solve the objective function.One kernel is the analytical kernel designed for solving analytical parameters.Another kernel is the automatic machine learning(AutoML)kernel designed to model LDS errors.The two kernels are connected with stepwise iterations to find the best calibration results.Compared with traditional methods,hand-eye experiments show that IABC reduces the calibration RMSE by about 50%.Verification experiments show that IABC reduces the measurement deviations by about 25%-50%and RMSEs within 40%.Even when the training data are obviously less than the test data,IABC performs well.Experiments demonstrate that IABC is more accurate than traditional hand-eye methods.
