In Situ Measurement of Spindle Radial and Tilt Error Motions by Complementary Multi-probe Method

This paper presents a complementary multi-probe method for measurement of radial and tilt error motions of a spindle. Neither indexing of artefact nor rotating of spindle housing is required and thus make it suitable for in situ evaluation of spindle performance effectively. In order to minimize the harmonic suppression problems commonly encountered in the multi-probe measurement approach, three sets of probe angle combinations were optimized and the harmonics of the three measurements were extracted and composed to reveal the true artefact errors in a complementary way. The exact probe angles were identified by the correlation function of the probe signals after the sensors are mounted onto the fixture and the requirement of high-precision fixtures was alleviated. The evaluation of measurement results showed that the erroneous harmonics were greatly reduced by 70%. Using this method, the radial error motions of the precision air bearing spindle were measured at seven axial positions and then the synchronized tilts error motions were calculated. This demonstrated an effective approach for measuring four degree-of-freedom error motions in one setup with a small number of displacement sensor probes.

Artificial Neural Network Modelling for Prediction of SNR Effected by Probe Properties on Ultrasonic Inspection of Austenitic Stainless Steel Weldments

Many austenitic stainless steel components are used in the construction of nuclear power plants. These components are joined by different welding processes, and radiation damages occur in the welds during the service life of the plant. The plants are inspected periodically with ultrasonic test methods. Many ultrasonic inspection problems arise due to the weld metal microstructure of austenitic stainless steel weldments. The present research was conducted in order to describe the affects of probe angle and probe frequency of both transversal and longitudinal wave probes on detecting the defects of austenitic stainless steel weldments. Feed forward back propagation artificial neural network (ANN) models have been developed for predicting signal to noise ratio (SNR) of transversal and longitudinal wave probes. Input variables that affect SNR output in these models are welding angle, probe angle, probe frequency and sound path. Of the experimental data, 80% is used for a training dataset and 20% is used for a testing dataset with 10 neurons in hidden layers in developed ANN models. Mean absolute error (MAE) and mean absolute percentage error (MAPE) types are calculated as 0.0656 and 16.28%, respectively, to predict performance of ANN models in a transversal wave probe. In addition, MAE and MAPE are calculated as 0.0478 and 18.01%, respectively, for performance in a longitudinal wave probe.

The Pre-Travel Error Study of Electrical Trigger Probe in On-Machine Measurement

On-machine measurement is a high-efficient measure method in NC machining, and there are pre-travel errors during measurement. What is more, these errors tend to present anisotropy, and affect the measurement accuracy. This paper aimed at measuring the anisotropy of probe’s pre-travel error. First, the standard ball is measured by changing the probe angle with the same machine tool moving direction, and the pre-travel errors with different probe angles under the same machine tool errors are obtained. Then, the machine tool backlash errors are measured and compensated, and the pre-travel errors with different probe angle are obtained. Finally, the mathematical model of pre-travel error is established. The method proposed in this paper can be used to determine probe’s pre-travel error with anisotropy.