桥梁基桩在含沙水流冲击作用下的冲蚀磨损数值模拟研究

过水桥梁的基桩外露已成为影响桥梁安全性的重要因素,携沙水流会对外露部分的基桩产生冲蚀作用,加速混凝土的破坏,累积到一定程度,会导致桥梁安全性降低,影响桥梁交通的正常运营。为研究携沙水流对基桩的冲蚀磨损规律及其影响因素,利用ANSYS-Workbench建立三维模型,在FLUENT的工作环境下,模拟水沙两相流的流动特性,并求解得出基桩最大冲蚀磨损速率。通过数值模拟,研究了携沙水流的流场特性,并得出了在不同流速、含沙量及粒径下的冲蚀磨损规律。结果表明:流速、含沙量及粒径都对最大冲蚀率有较大影响,其中,最大冲蚀率随流速的增加而增大,并呈现出明显的指数关系;含沙量与最大冲蚀率呈现出正相关关系;最大冲蚀率随粒径的增大呈现出先减少后趋于稳定的趋势。研究成果可以为后续基桩冲蚀磨损预测的工程应用提供相关依据。     

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.     

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.     

基于机器视觉的加工刀具磨损监测方法

为了对加工过程中刀具的磨损状态进行监测,针对麻花钻的磨损形式,提出基于机器视觉的加工刀具磨损监测方法. 根据磨损刀具图像的灰度分布特点,提出基于积分图加速和Turky bi-weight核函数的非局部均值去噪方法;采用单、双阈值大津法获取磨损区域的灰度区间,实现对图像的自适应对比度增强;提出基于形态学重构方法的磨损区域局部极值点提取方法,有效完成对磨损区域的检测和边界提取. 该刀具磨损检测方法成功应用于麻花钻头磨损状态的监测过程,实验结果表明,相较于目前已有的机器视觉监测方法,所提出的方法具有更高的检测精度和效率,准确地提取磨损轮廓,从而有效实现对刀具磨损状态的监测和自动化监控加工过程,达到降低人工成本和产品不合格率的目的.     

碳化物在高铬铸铁高应力磨料磨损中的影响

对不同载荷作用下的高铬铸铁在耐磨实验机上进行了磨料磨损试验;对在不同的碳化物对基体的保护作用与碳化物的剥离进行了相关的分析.结果表明载荷在一定值以下磨损较为缓慢,当载荷超过一定值时,磨损急剧加剧,随着载荷的增加"尺寸较小"的碳化物首先剥离基体,当载荷继续增大时,"尺寸较大"的碳化物也剥离基体并有碎裂现象.这要求高铬铸铁中的碳化物不仅要分布均匀,碳化物的尺寸也要合适.     

某600 MW机组SCR脱硝装置喷氨管道磨损数值模拟分析

电站锅炉飞灰不均匀磨损会大幅降低设备的使用寿命。针对某600 MW机组选择性催化还原脱硝装置喷氨管道不均匀磨损问题,利用计算流体力学数值模拟计算软件完成了脱硝装置的磨损分析。结果表明,数值计算得到的喷氨管道严重磨损区域与实际检修过程中发现的严重磨损区域相符,说明数值模拟分析可用于分析复杂烟气环境下的磨损问题。计算结果表明,喷氨管道的主要磨损区域在距离烟道壁面200 mm范围以内,最大磨损速率达到3.22×10^-7kg/(m^2·s),最大磨损速率是平均磨损速率的4019倍。通过最大磨损速率计算管道磨损失效时间为40313 h,与实际磨穿失效时间基本相符,说明数值模拟计算也可作为定量分析复杂烟气环境磨损问题的一个重要工具。     

Perpetual effect of laser surface texturing on tribological properties of Ti3SiC2/GNP composite—A fretting wear study

Tribological properties of Ti₃SiC₂ ceramic and Ti₃SiC₂/GNP composite were investigated using fretting sliding against Si₃N₄ ball counter body at a load of 50 N for a sliding distance of 900 m, under non-textured lubricated (NTL), textured lubricated (TL) condition, and textured lubricated high temperature (TLHT). This paper presents the influence of laser surface texturing (LST) on Ti₃SiC₂ and its graphene nanoplatelets (GNP) reinforced composite established through spark plasma sintering (SPS), tested under fretting wear conditions. The coefficient of friction and wear rate of Ti₃SiC₂ and its graphene nanoplatelets reinforced composites were incontrovertibly enhanced with laser surface texturing as compared to non-textured Ti₃SiC₂ and its GNP composite. The TL surface of Ti₃SiC₂ composite reinforced with 15% GNP showed the lowest COF and wear rate. Almost negligible changes were observed for TLHT tests. The LST proved to an efficient technique for enhancing the friction and wear properties of Ti₃SiC₂ and Ti₃SiC₂/GNP composite.     

耐磨新材料新技术研究开发与耐磨材料产业化--第十届全国耐磨材料大会述评

介绍了第十届全国耐磨材料大会概况.分耐磨钢铁材料、耐磨复合材料、耐磨表面工程、材料的摩擦和磨损、耐磨材料产业化等方面评述了会议报告与论文.简述了我国耐磨材料行业的现状,并就提高我国耐磨材料行业技术和管理水平提出了建议.