High-performance Self-lubricating Ceramic Composites with Laminated-graded Structure

High-performance ceramic composites are potential candidates for the application of wear-resistance components because of their excellent properties. Nevertheless, many problems, such as high friction coefficient of ceramic material and poor mechanical properties of ceramic-matrix self-lubricating composites, limit a wider range of applications of these composites in tribological areas. Therefore, improving high-toughness ceramic-matrix self-lubricating materials for practical applications is significant. This study proposes a new design for ceramic self-lubricating composites to overcome the conflict between their mechanical and tribological properties. Complying with the design principle of bionic and graded composites, two kinds of self-lubricating ceramic composites with laminated-graded structure were prepared, and their mechanical and tribological properties were studied. The results show that this newly developed ceramic composite has achieved satisfactory strength and tribological properties compared with the traditional ceramic self-lubricating composites. The bending strength reached the same level as the properties of general monolithic ceramics. In the temperature range of 25-800 °C, the friction coefficient of composites was less than 0.55, which was about half of that of monolithic ceramics

Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks

In temporal-difference reinforcement learning algorithms, variance in value estimation can cause instability and overestimation of the maximal target value. Many algorithms have been proposed to reduce overestimation, including several recent ensemble methods, however none have shown success in sample-efficient learning through addressing estimation variance as the root cause of overestimation. In this paper, we propose MeanQ, a simple ensemble method that estimates target values as ensemble means. Despite its simplicity, MeanQ shows remarkable sample efficiency in experiments on the Atari Learning Environment benchmark. Importantly, we find that an ensemble of size 5 sufficiently reduces estimation variance to obviate the lagging target network, eliminating it as a source of bias and further gaining sample efficiency. We justify intuitively and empirically the design choices in MeanQ, including the necessity of independent experience sampling. On a set of 26 benchmark Atari environments, MeanQ outperforms all tested baselines, including the best available baseline, SUNRISE, at 100K interaction steps in 16/26 environments, and by 68% on average. MeanQ also outperforms Rainbow DQN at 500K steps in 21/26 environments, and by 49% on average, and achieves average human-level performance using 200K ($\pm$100K) interaction steps. Our implementation is available at https://github.com/indylab/MeanQ

Improved Tribological Performance of Amorphous Carbon (a-C) Coating by ZrO2 Nanoparticles

Nanomaterials, such as Graphene, h-BN nanoparticles and MoS2 nanotubes, have shown their ability in improving the tribological performance of amorphous carbon (a-C) coatings. In the current study, the effectiveness of ZrO2 nanoparticles (ZrO2-NPs) in lubricating the self-mated nonhydrogenated a-C contacts was investigated in boundary lubrication regime. The results showed that 13% less friction and 50% less wear compared to the base oil were achieved by employing ZrO2-NPs in the base oil in self-mated a-C contacts. Via analyzing the ZrO2-NPs and the worn a-C surface after tests, it was found that the improved lubrication by ZrO2-NPs was based on “polishing effects”, which is a new phenomenon observed between a-C and nanoparticles. Under the “polishing effect”, micro-plateaus with extremely smooth surface and uniform height were produced on the analyzed a-C surface. The resulting topography of the a-C coating is suitable for ZrO2-NPs to act as nano-bearings between rubbing surfaces. Especially, the ZrO2-NPs exhibited excellent mechanical and chemical stability, even under the severe service condition, suggesting that the combination of nonhydrogenated a-C coating with ZrO2-NPs is an effective, long lasting and environment-friendly lubrication solution

Reliability analysis of computed tomography equipment using the q‐Weibull distribution

Abstract Inspired by the successful application of the q‐Weibull distribution in other research fields, we took the lead to use it in the field of medical devices in this work. The parameter estimation of the q‐Weibull distribution was performed using the probability plot method. The CT failure data from Nanfang Hospital in Guangzhou, China, were used to study the reliability of CT equipment at two levels: the CT system and its seven main components. In terms of evaluation accuracy, the mean squared error, Akaike's information criterion, and the determination coefficient were used to compare the accuracy of fitting of different distribution models. The results show that the accuracy of fitting the q‐Weibull distribution is higher than that of the Weibull distribution in terms of determination coefficient and mean squared error. When considering the complexity of the model, the fit accuracy of the Weibull distribution is better. The results were analyzed using reliability and failure rate plots. The q‐Weibull distribution gives a good fit for the failure data of the CT system and components. Though the Weibull distribution fits better in a few cases, the q‐Weibull distribution can describe the entire “bathtub curve” with only a set of parameters. The findings of this study can be extended to other medical devices