Tribological behavior and applications of carbon fiber reinforced ceramic composites as high-performance frictional materials

Due to the favorable tribological, mechanical, chemical, and thermal properties, carbon fiber reinforced ceramic composites, especially carbon fiber reinforced carbon and silicon carbide dual matrix composites (C/C–SiC), has been considered as high-performance frictional materials. In this paper, current applications and recent progress on tribological behavior of C/C–SiC composites are reviewed. The factors affecting the friction and wear properties, including the content of silicon carbide and carbon matrix, carbon fiber preform architecture, as well as the matrix modification by alloy additives and C/C–SiC composites under various test conditions are reviewed. Furthermore, based on the current status of researches, prospect of several technically available solutions for low-cost manufacturing C/C–SiC composites is also proposed.     

Sintering behavior of Ni–Zn ferrite powders prepared by molten-salt synthesis

Ni_0.5Zn_0.5Fe₂O₄ powders were prepared by a novel molten-salt synthesis method. The effects of calcination processes of the powders on their sintering behaviors were investigated. Compared with the synthesis by traditional solid-state reaction, the proposed molten-salt method can significantly reduce the synthesis temperature of Ni_0.5Zn_0.5Fe₂O₄ from 800 to 550°C below, and the prepared powders have relatively high sintering activity at low temperature, which can thus decrease the sintering temperature. However, the abnormal growth of grains is easy to occur during sintering, thus resulting in uneven grain size. In particular, during the molten-salt synthesis, the holding time for calcination is a dominant factor affecting the activity and crystallization degree of the resultant powders. From the point of view of increasing the density of sintered bodies, the optimal conditions for synthesizing Ni_0.5Zn_0.5Fe₂O₄ powder by the proposed molten-salt synthesis is 400°C for 6 h. In addition, the saturate magnetization of the finally obtained ferrite ceramics has nothing to do with the preparation processes, while their coercivity depends on their densification and grain size caused by their different processing routes.     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.     

Towards artificial intelligence at scale in the chemical industry

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

钕铁硼化学镀防护及在模拟海洋大气环境中的腐蚀行为

采用化学镀方法在钕铁硼表面分别制备Ni-P合金镀层、Ni-Mo-P合金镀层、Ni-P/PTFE复合镀层和Ni-Mo-P/PTFE复合镀层,并研究了不同化学镀层在模拟海洋大气环境中的腐蚀行为。结果表明:Ni-P合金镀层、Ni-Mo-P合金镀层、Ni-P/PTFE复合镀层和Ni-Mo-P/PTFE复合镀层都完整覆盖钕铁硼表面,它们的粗糙度差别不大,在模拟海洋大气环境中的腐蚀失重都低于钕铁硼的腐蚀失重,容抗弧半径增大且电荷转移电阻有不同程度的提高。与Ni-P合金镀层和Ni-Mo-P合金镀层相比,Ni-P/PTFE复合镀层和Ni-Mo-P/PTFE复合镀层具有优良的耐腐蚀性能,原因在于PTFE颗粒较均匀的沉积在镀层表面增加一道屏蔽层,也起到阻碍腐蚀介质渗透腐蚀的作用。尤其是Ni-Mo-P/PTFE复合镀层,其表面更致密,PTFE颗粒沉积更均匀,能更有效延缓腐蚀介质与钕铁硼接触,显著提高钕铁硼在模拟海洋大气环境中的耐腐蚀性能。     

[Zr0.73(Cu0.59Ni0.41)0.27]87Al13合金过冷液相区热塑性成形研究

为探究[Zr_(0.73)(Cu_(0.59)Ni_(0.41))_(0.27)]_(87)Al_(13)非晶合金的热塑性成形性能以及绘制其对应的热加工图谱,用Gleeble3500型热模拟压缩实验机对该非晶合金进行不同参数下的热模拟压缩实验。结果表明,合金在压缩过程中变形行为由牛顿流变演变为非牛顿流变;同时,过高或过低的热加工温度均能导致合金晶化;进一步对数据分析得到该合金在不同热塑性成形参数下的功率耗散图与流变失稳图,并绘制出相应的热加工谱图,谱图分析结果表明,该合金在温度为420与430℃、应变速率为10~(-3) s~(-1)时具有较高功率耗散系数且没有失稳区域,因此,合金可选的热塑性加工参数为温度420～430℃,应变速率10~(-3) s~(-1)。     

变分自编码器模型综述

变分自编码器（VAE）作为深度隐空间生成模型的一种，近年来其表现性能取得了极大的成功，尤其是在图像生成方面。变分自编码器模型作为无监督式特征学习的重要工具之一，可以通过学习隐编码空间与数据生成空间的特征映射，进而在输出端重构生成输入数据。梳理了传统变分自编码器模型及其衍生变体模型的发展与研究现状，并就此做了总结和对比，最后分析了变分自编码器模型存在的问题与挑战，并就可能的发展趋势做了展望。     

Nonlinear modal analysis via non‐parametric machine learning tools

Modal analysis is an important tool in the structural dynamics community; it is widely utilised to understand and investigate the dynamical characteristics of linear structures. Many methods have been proposed in recent years regarding the extension to nonlinear analysis, such as nonlinear normal modes or the method of normal forms, with the main objective being to formulate a mathematical model of a nonlinear dynamical structure based on observations of input/output data from the dynamical system. In fact, for the majority of structures where the effect of nonlinearity becomes significant, nonlinear modal analysis is a necessity. The objective of the current paper is to demonstrate a machine learning approach to output‐only nonlinear modal decomposition using kernel independent component analysis and locally linear‐embedding analysis. The key element is to demonstrate a pattern recognition approach which exploits the idea of independence of principal components from the linear theory by learning the nonlinear manifold between the variables. In this work, the importance of output‐only modal analysis via “blind source” separation tools is highlighted as the excitation input/force is not needed and the method can be implemented directly via experimental data signals without worrying about the presence or not of specific nonlinearities in the structure.