During accident, the interlayer of windshield plays an important role in the crash safety of automotive and protection of pedestrian or passenger. The understanding of its energy absorption capability is of fundamental importance. Conventional interlayer material of automotive windshield is made by Polyvinyl butyral (PVB). Recently, a new candidate of high-performance nanoporous energy absorption system (NEAS) has been suggested as a candidate for crashworthiness. For the model problem of pedestrian head impact with windshield, we compare the energy absorption capabilities of PVB and NEAS interlayers, in terms of the contact force, acceleration, velocity, head injury criteria, and energy absorption ratio, among which results obtained from PVB interlayers are validated by literature references. The impact speed is obtained from virtual test field in PC-CRASH, and the impact simulations are carried out using explicit finite element simulations. Both the accident speed and interlayer thickness are varied to explore their effects. The explicit relationships established among the energy absorption capabilities, impact speed, and interlayer material/thickness, are useful for safety evaluation as well as automotive design. It is shown that the NEAS interlayer may absorb more energy than PVB interlayer and it may be a competitive candidate for windshield interlayer.

As the core component of the subway running gear, the safety and reliability of the bearing has been widely concerned. Under the influence of high speed, heavy load, long routing, sand erosion, rain corrosion and other operating conditions, the service conditions of bearings become very harsh, and damage inevitably occurs. At present, most of the nondestructive testing technologies used in metro bearings are black magnetic particle testing technology, which has the advantages of high sensitivity, high reliability and easy identification of defects. It is one of the most effective technologies widely recognized for the effective inspection of the surface or near-surface of ferromagnetic materials. To detect the surface defects of rolling bearings of metro vehicles, the images of black magnetic particle flaw detection defects of metro vehicles’ rolling bearings are collected on the spot. By using image processing technology, all binary images of surface defects are extracted, and a data set including the original image and binary images of surface defects is established. It is pointed out that there are great difficulties and limitations in using traditional machine vision technology to identify subway bearing flaw detection defects. Thus, convolutional neural network is chosen to identify bearing defects to realize deep feature fusion. Through the comparison with several models, it is concluded that there is a certain improvement in the effect and accuracy of surface defect identification in this paper, which can not only reduce the labor intensity and training costs of personnel, but also ensure the efficiency and accuracy of bearing defect detection identification. It is the trend and hot spot of future development.

In this paper, energy absorption characteristics of PVB laminated windshield subject to human head impact are studied. SHPB method is carried out to obtain the constitutive relationships of PVB laminated glass in dynamic behavior. With the SHPB results embedded, finite element simulation is used to study the dynamic behavior of PVB laminated windshield. In particular, energy absorption characteristics are investigated. Two parameters for measuring the energy absorption property of windshield are suggested, i.e. loss of head velocity and HIC value and a parametric study is carried out to see the effect of impact velocity and impact position. Results can shed lights on the research of energy absorption capability of PVB laminated windshield.

The compactly supported radial basis functions (RBFs) is modified and used to the wave propagation in the anisotropic materials. An example to simulate the wave propagation in composite material is used in the paper to verify this method. In this example, stress wave propagation histories are obtained. The comparison between results by this method and by finite element method is also made. And the agreement with two results shows that this method can be used to simulate the wave propagation history in anisotropic material efficiently.