In the ultraprecision vibration isolation systems, it is desirable for the isolator to have a larger load bearing capacity and a broader isolation bandwidth simultaneously. Generally, pneumatic spring can bear large load and achieve relatively low natural frequency by enlarging its chamber volume. However, the oversized isolator is inconvenient to use and might cause instability. To reduce the size, a miniaturized pneumatic vibration isolator (MPVI) with high-static-low-dynamic stiffness (HSLDS) is developed in this paper. The volume of proposed isolator is minimized by a compact structure design that combines two magnetic rings in parallel with the pneumatic spring. The two magnetic rings are arranged in the repulsive configuration and can be mounted into the chamber to provide the negative stiffness. Then dynamic model of the developed MPVI is built and the isolation performances are analyzed. Finally, experiments on the isolator with and without the magnetic rings are conducted. The final experimental results are consistent with the dynamical model and verify the effectiveness of the developed vibration isolator.

This article proposes a new piezoelectric structure for energy harvesting from flow-induced vibrations. It consists of a properly poled and electroded flexible ceramic cylinder. When it is in a flow perpendicular to its axis, the flow exerts a transverse force on the cylinder due to asymmetric vortex shedding, which drives the cylinder into flexural vibrations with an electrical output. A one-dimensional model is derived for the motion of the cylinder, which allows an analytical solution from which the basic behaviors of the energy harvester are calculated and examined. For a cylinder of 40 cm in length and $1$ cm in diameter in flowing air with a speed of 5 m/s, the output power is of the order of ${10}^{-3}\text{W}$ . It becomes significantly higher if the flow speed is increased.

Magnetic springs using permanent magnets (PMs) have shown advantages in various fields. For instance, they are integrated into precision positioning actuators for passive gravity compensation, as well as into precision vibration isolators to reduce dynamic stiffness. In this article, an inverse design method of magnetic springs is proposed, which enables to customize the force–displacement relationship by shaping PMs. First, a concept of modulated magnetic spring is introduced, whose stator PMs are nonuniformly magnetized. The modulation method is developed to customize the force–displacement relationship. Second, uniformly magnetized PMs are shaped layer by layer to substitute the modulated PM. This process involves optimizing the size of each layer independently to generate a nearly equivalent force, resulting in a highly manufacturable special-shaped magnetic spring. Three design examples are presented and tested. The first two examples maintain a constant force and a constant negative stiffness, respectively, within 30 mm. The relative errors are only 3.7% and 2.3%, respectively, which are defined as the ratio between the maximum deviation and the mean value. The third example can compensate for the nonlinear force of the subsidiary structures and maintain a constant resultant force in 40 mm with a relative error of 2.9%. This method is confirmed to customize the force–displacement relationship for various applications.

The defects in the welds of energy pipelines have significantly influenced their safe operation. The inefficient and inaccurate detection of the defects may give rise to catastrophic accidents. Ultrasonic phased array inspection is an important means of ensuring pipeline safety. The total focusing method (TFM), using ultrasonic phased arrays, has become widely used in recent years in non-destructive evaluation (NDE). However, manual defect recognition of TFM images is seen to lack accuracy and robustness, arising from deficiency of practical experience. In this paper, the automated classification of different defects from TFM images is studied with a view to facilitate inspection efficacy. By experimentally implementing the TFM approach on a bespoke specimen, the images corresponding to crack-like defects and pore-like defects were employed to investigate the effectiveness of four different machine learning models (known as Support Vector Machine, CART Decision tree, K Nearest Neighbors, Naive Bayes) containing data augmentation, feature extraction and defect classification. The results suggested that the accuracy of defect classification using the HOG-Poly-SVM algorithm was 93%, which outperformed the results from other algorithms. The advantage of the HOG-Poly-SVM algorithm used in defect classification of ultrasonic phased array TFM data is discussed by conducting ten-fold cross validation and other evaluation metrics. In this paper, in order to improve the efficiency of detecting pipeline defects in the future, and for testing test blocks simulating buried pipelines containing defects, we proposed, for the first time, that ultrasonic phased-array TFM imaging results in small object detection images, and found that the SVM algorithm was applicable to ultrasonic phased array TFM imaging, providing a research method and ideas for the use of artificial intelligence in industrial non-destructive testing.