基于BA-WPHM的滚动轴承两阶段剩余寿命预测方法

为了提高滚动轴承剩余寿命预测的准确性,根据滚动轴承运行过程的两阶段性特点,提出了一种基于蝙蝠算法(BA)和威布尔比例风险模型(WPHM)的滚动轴承两阶段剩余寿命预测方法。首先,构建基于WPHM的剩余寿命预测模型;其次,提出了两阶段极大似然估计法,建立新的似然函数,并利用BA算法进行求解,以提高参数估计的准确性;最后,建立BA-WPHM模型对滚动轴承进行剩余寿命预测。案例分析表明,相比于Newton-Raphson算法、自组织分层猴群算法(SHMA)和独特的自适应粒子群算法(UAPSO),提出的方法参数估计的准确性更高,剩余寿命的预测精度优于支持向量回归(SVR)方法,验证了所提方法的有效性,为滚动轴承维修决策的可行性提供了依据。     

Physics-guided logistic classification for tool life modeling and process parameter optimization in machining

This paper describes a physics-guided logistic classification method for tool life modeling and process parameter optimization in machining. Tool life is modeled using a classification method since the exact tool life cannot be measured in a typical production environment where tool wear can only be directly measured when the tool is replaced. In this study, laboratory tool wear experiments are used to simulate tool wear data normally collected during part production. Two states are defined: tool not worn (class 0) and tool worn (class 1). The non-linear reduction in tool life with cutting speed is modeled by applying a logarithmic transformation to the inputs for the logistic classification model. A method for interpretability of the logistic model coefficients is provided by comparison with the empirical Taylor tool life model. The method is validated using tool wear experiments for milling. Results show that the physics-guided logistic classification method can predict tool life using limited datasets. A method for pre-process optimization of machining parameters using a probabilistic machining cost model is presented. The proposed method offers a robust and practical approach to tool life modeling and process parameter optimization in a production environment.     

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network. First, a parallel residual network (PResNet) is used to extract the multi-scale local features of sensor signals adaptively. Subsequently, a stacked bidirectional long short-term memory (SBiLSTM) network is used to obtain the time-series features related to the tool wear characteristics. Finally, the predicted tool wear value is outputted through a fully connected network. A smoothing correction method is applied to improve the prediction accuracy. The proposed model is experimentally verified to have a high prediction accuracy without sacrificing its generalization ability. A TWM system framework based on the PRes–SBiLSTM network is proposed, which has a certain reference value for TWM in actual industrial environments.     

基于蚁群优化的WSN能耗均衡链状路由协议

为了从路由技术的角度解决无线传感器网络的能耗问题,综合蚁群优化算法和PEGASIS协议的思想提出了ACO-PEGASIS路由协议;该协议采用蚁群优化算法构建通信链,解决了PEGASIS协议中由于贪婪算法的局部性产生的相邻节点间的长链问题;并在成链过程中综合考虑节点间距离、节点剩余能量等因子,以均衡全网的能量消耗;同时根据距离和能量因素选取链头节点并采用一定措施降低链头重选次数;仿真结果表明,与PEGASIS协议相比,该协议更加有效地均衡了全网的能量消耗,延长了网络的生存周期。     

合金工具钢二次硬化能力的判别

为寻求在钢的成份和二次硬化能力之间的定量关系,对国内外40多种钢按作者提出的合金化参数进行计算和整理,证明了用“平衡碳”(Cp)和“碳饱和度”(A) 两个合金化参数判别 (预报) 钢的二次硬度的可能性。     

浅议进一步发挥军事代表在武器装备全寿命费用管理中的作用

阐述了军事代表参与武器装备全寿命费用管理的现状和作用,提出了提高军事代表费用管理参与率的措施.     

The statistical modeling of surface roughness in high-speed flat end milling

Surface roughness is one of the most important requirements in machining process. The surface roughness value is a result of the tool wear. When tool wear increase, the surface roughness also increases. The determination of the sufficient cutting parameters is a very important process obtained by means of both minimum surface roughness values and long tool life. The statistical models were developed to predict the surface roughness.This paper presents the development of a statistical model for surface roughness estimation in a high-speed flat end milling process under wet cutting conditions, using machining variables such as spindle speed, feed rate, depth of cut, and step over. First- and second-order models were developed using experimental results of a rotatable central composite design, and assessed by means of various statistical tests. The highest coefficient of correlation (R_adj²) (88%) was obtained with a 10-parameter second-order model. Meanwhile, a time trend was observed in residual values between model predictions and experimental data, reflecting the probable effect of the tool wear on surface roughness. Thus, in order to enhance the estimation capability of the model, another independent variable was included into the model to account for the effect of the tool wear, and the total operating time of the tool was selected as the most suitable variable for this purpose. By inserting this new variable as a linear term into the model, R_adj² was increased to 94% and a good fit was observed between the model predictions and supplementary experimental data.In this study, it was observed that, the order of significance of the main variables is as X₅>X₃>X₄>X₁>X₂ (total machining time, depth of cut, step over, spindle speed and feed rate, respectively).     

Novel methods for rapid assessment of tool performance in milling

The paper examines the effectiveness of two innovative techniques designed to rapidly optimize a milling application. One of them relates to quantifying the relative wear of different insert grades concurrently in a single cutting test, by mounting the inserts in the same cutter, for a quick comparative performance evaluation. Experimental results that illustrate the validity and limitation of this concept, and a scheme for enhancing the reliability of the test method are presented. The other technique refers to rapid identification of the optimum feed/tooth that corresponds to maximum tool life. This entails a test wherein individual inserts in the cutter are subject to feed/tooth that are multiples of a base value, by selectively leaving appropriate number of consecutive insert pockets unoccupied. These novel techniques complement known accelerated tool life tests, and are expedient for industries that engage short production runs, in terms of selecting a suitable insert grade for an application, and determining optimal cutting conditions for the selected grade.     

Evaluation of the Effects of Developmental Trauma on Neurotransmitter Systems Using Functional Molecular Imaging

Early life stress (ELS) is strongly associated with psychiatric disorders such as anxiety, depression, and schizophrenia in adulthood. To date, biological, behavioral, and structural aspects of ELS have been studied extensively, but their functional effects remain unclear. Here, we examined NeuroPET studies of dopaminergic, glutamatergic, and serotonergic systems in ELS animal models. Maternal separation and restraint stress were used to generate single or complex developmental trauma. Body weights of animals exposed to single trauma were similar to those of control animals; however, animals exposed to complex trauma exhibited loss of body weight when compared to controls. In behavioral tests, the complex developmental trauma group exhibited a decrease in time spent in the open arm of the elevated plus-maze and an increase in immobility time in the forced swim test when compared to control animals. In NeuroPET studies, the complex trauma group displayed a reduction in brain uptake values when compared to single trauma and control groups. Of neurotransmitter systems analyzed, the rate of decrease in brain uptake was the highest in the serotonergic group. Collectively, our results indicate that developmental trauma events induce behavioral deficits, including anxiety- and depressive-like phenotypes and dysfunction in neurotransmitter systems.     

Post-Translational Modifications of Retroviral HIV-1 Gag Precursors: An Overview of Their Biological Role

Protein post-translational modifications (PTMs) play key roles in eukaryotes since they finely regulate numerous mechanisms used to diversify the protein functions and to modulate their signaling networks. Besides, these chemical modifications also take part in the viral hijacking of the host, and also contribute to the cellular response to viral infections. All domains of the human immunodeficiency virus type 1 (HIV-1) Gag precursor of 55-kDa (Pr55^Gag), which is the central actor for viral RNA specific recruitment and genome packaging, are post-translationally modified. In this review, we summarize the current knowledge about HIV-1 Pr55^Gag PTMs such as myristoylation, phosphorylation, ubiquitination, sumoylation, methylation, and ISGylation in order to figure out how these modifications affect the precursor functions and viral replication. Indeed, in HIV-1, PTMs regulate the precursor trafficking between cell compartments and its anchoring at the plasma membrane, where viral assembly occurs. Interestingly, PTMs also allow Pr55^Gag to hijack the cell machinery to achieve viral budding as they drive recognition between viral proteins or cellular components such as the ESCRT machinery. Finally, we will describe and compare PTMs of several other retroviral Gag proteins to give a global overview of their role in the retroviral life cycle.