3D printing technology is widely used in the field of implantable medical device in recent decades because of its advantages in high precision, complex structure, and high material utilization. Based on the characteristics of 3D printing technology, this paper reviews the manufacturing process, materials, and some typical products of 3D printing implantable medical devices and analyzes and summarizes the development trend of 3D printed implantable medical devices.

In order to deal with the shortcomings (such as poor robustness) of the traditional single-channel vibration signal in the comprehensive monitoring of the gearbox fault state, a multi-channel decision-level fusion algorithm was proposed based on symmetrized dot pattern (SDP) analysis, with the visual geometry group 16 network (VGG16) fault diagnosis model. Firstly, the SDP method was used to convert the vibration signal of a single multi-channel sensor into an imaging arm. Secondly, the obtained image arm was input into the VGG16 convolutional neural network in order to train the fault diagnosis model that can be obtained. Then, the SDP images of the signals that were to be measured from multiple multi-channel sensors were input into the fault diagnosis model, and the diagnosis results of multiple multi-channel sensors could then be obtained. Experimentally, it was demonstrated that the diagnostic results of multi-channel sensors one, two, and three were more accurate than those of single-channel sensors one, two, and three, by 3.01%, 16.7%, and 5.17%, respectively. However, the fault generation was not generated in a single direction, but rather multiple directions. In order to improve the comprehensiveness of the raw vibration data, a fusion method using DS (Dempster–Shafer) evidence theory was proposed in order to fuse multiple multi-channel sensors, in which the accuracy achieved 99.93% when sensor one and sensor two were fused, which was an improvement of 8.88% and 1.02% over single sensors one and two, respectively. When sensor one and sensor three were fused, the accuracy reached 99.31%, which was an improvement of 8.31% and 6.17% over single sensors one and three, respectively. When sensor two and sensor three were fused, the accuracy reached 99.91%, which was an improvement of 1.00% and 6.74% over single sensors two and three, respectively. When three sensors were fused simultaneously, the accuracy reached 99.99%, which was 8.93%, 1.08%, and 6.81% better than single sensors one, two, and three, respectively. Therefore, it can be proved that the number of sensor channels has a great influence on the diagnosis results.

To address the problem of the low recognition rate of time-frequency domain methods gearbox fault identification, a method featuring decision-level fusion of DS evidence theory and GA-BP algorithm was proposed in the present study. Firstly, the fault data of each state of the gearbox was classified, based on which the time-frequency domain features were extracted and 19 significant features have been selected. Secondly, the accuracy of the traditional BP algorithm was compared with that of the GA-BP algorithm. On this basis, it has been concluded that the GA-BP algorithm is highly accurate, and the local diagnostic results obtained by the GA-BP algorithm have been used as the basic probability. Finally, the DS evidence theory is currently used to fuses with the GA. In addition, the final fault identification of the gearbox can be achieved by using the DS evidence theory and the multi-sensor local diagnosis results obtained by the GA-BP algorithm for decision fusion. The results of the simulations and experiments showed that the method proposed has improved accuracy over a single algorithm for fault identification of gearboxes, respectively.

In this study, an integrated gray-level gradient method is applied to extract the three-dimensional (3D) velocity fields of sprays. This method consists of a conventional edge-sharpness method and a new method, namely, the overall-sharpness method, which is an efficient supplement of the former. And then the synchronization system is designed and assembled to record double-exposure spray holograms in a short time interval. Finally, using the integrated gray-level gradient method and some image processing techniques, the 3D coordinates of droplets are easily obtained, which can be used to evaluate the 3D velocity fields and the size features of spray droplets in different spray injection pressures. It proves that the integrated gray-level gradient method is well applied to measure the characteristics of sprays in in-line digital holography.

In this study, a two-step pack cementation process (preboronizing and then chromizing) was employed to prepare the B-Cr duplex-alloyed coating on the steel. After the first step of preboronizing (PB sample), box-type furnace chromizing (BC-1 sample) and induction heating chromizing (BC-2 sample) were carried out, respectively. The phases and microstructure of the coatings were characterized by X-ray diffraction (XRD), backscattering electron imaging (BSEI), and energy dispersive spectroscopy (EDS). The results reveal that the heating mode of the second step of chromizing has a significant effect on the phase composition and microstructure of the B-Cr coating. The efficiency of induction heating is higher than that of the box furnace heating, resulting in a thicker, denser, flatter surface, and B-Cr coating with fully reacted B and Cr elements. The wear and corrosion resistance of the steel is found to be significantly improved by the formation of effective B-Cr coating. The formation mechanisms and properties of the two duplex-alloyed coatings are investigated and discussed.

A decision-level approach using multi-sensor-based symmetry dot pattern (SDP) analysis with a Visual Geometry Group 16 network (VGG16) fault diagnosis model for multi-source information fusion was proposed to realize accurate and comprehensive fault diagnosis of gearbox gear teeth. Firstly, the SDP technique was used to perform a feature-level fusion of the fault states of gearbox gear collected by multiple sensors, which could initially visualize the vibration states of the gear teeth in different states. Secondly, the SDP images obtained were combined with the deep learning VGG16. In this way, the local diagnostic results of each sensor can be easily obtained. Finally, the local diagnostic results of each sensor were combined with the DS evidence theory to achieve decision-level fusion, which can better realize comprehensive fault detection for gearbox gear teeth. Before fusion, the accuracies of the three sensors were 96.43%, 93.97%, and 93.28%, respectively. When sensor 1 and sensor 2 were fused, the accuracy reached 99.93%, which is 3.52% and 6.34% better than when using sensors 1 and 2, respectively, alone. When sensor 1 and sensor 3 were fused, the accuracy reached 99.96%, marking an improvement of 3.36% and 6.85% over individual use of sensors 1 and 3, respectively. When sensor 2 and sensor 3 were fused, the accuracy reached 99.40%, which is 5.78% and 6.56% better than individual use of sensors 2 and 3, respectively. When the three sensors were fused simultaneously, the accuracy reached 99.98%, which is 3.68%, 6.40%, and 7.18% better than individual use of sensors 1, 2, and 3, respectively.