斜井提升钢丝绳磨损“失效树”分析

介绍了引起斜井提升钢丝绳失效的各种因素 ,并建立“失效树” ,为煤矿生产中判断其失效原因及预防提供可靠依据。     

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.     

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.     

SiC含量对SiCp/Al复合材料摩擦性能的影响

采用粉末冶金技术制备了SiC_p/Al复合材料,探讨了SiC颗粒质量分数对SiC_p/Al复合材料密度、布氏硬度、微观形貌以及摩擦磨损性能的影响。结果表明,SiC颗粒表面形成了少量可提高界面结合性的Al₄C₃化合物。随着SiC质量分数增加,SiC_p/Al复合材料的密度没有明显的变化,当SiC质量分数增加至25%时,密度明显下降。SiC_p/Al复合材料的布氏硬度随着SiC质量分数的增加呈先增长后减小的变化趋势。当SiC质量分数为20%时,材料的硬度最优（HBW 114）,平均摩擦系数达到最大值（0.3425）,摩擦后试样表面形貌平整且犁沟较浅,SiC颗粒未出现明显剥落。     

Nb微合金化汽车用TWIP钢的研究进展

汽车是日常生活中主要交通工具之一,其用钢质量的优劣直接关系到汽车本身及乘坐人员的安全,因此研发高性能汽车用钢至关重要。微合金化是有效改善汽车用钢性能的手段之一,微合金元素铌可细化晶粒,提高材料的强韧性及氢致延迟断裂性能,备受研究者的青睐。总结了微合金元素铌对汽车用TWIP钢组织的影响,综述了铌对汽车TWIP钢力学性能、耐磨性能及抗氢致延迟断裂性能等的作用及相应机制,并提出了现阶段铌微合金化汽车用TWIP钢研究过程中存在的问题,为后续低成本、高效地发挥铌元素在高强度汽车钢中的应用提供参考依据。