Morphological aspects of rubber fracture and wear

Morphology of carbon black (CB)‐filled rubber was studied with AFM and SEM and the classification of filler agglomerates, based on their internal structure and filler–matrix interactions, has been proposed. It varies according to the activity of CB and the kind of rubber. It has been shown that fracture of rubber starts either inside filler agglomerates (de‐cohesion) or in a filler–matrix interphase (de‐adhesion) initiating wear of the material. Microscopic observations correlate well with TGA and EPR data of wear debris collected during friction of rubber. The increase of thermal stability of CB and the appearance of additional spin signals, respectively, for elongated samples or debris, are proposed to be associated with de‐cohesion or de‐adhesion of the agglomerates. New approach to the fracture of rubber called “fatal agglomerate concept” is presented. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

油润滑下Al/MoSi2材料的摩擦学特性

运用M-200型磨损试验机测定了油润滑下Al/MoSi2材料的摩擦磨损性能，采用SEM和X射线观察与分析了摩擦副表面的形貌和相组成，探讨了磨损机制。结果表明：采用20#机油润滑可有效地提高Al/MoSi2材料的摩察磨损综合性能；与干摩擦状态相比，其摩擦系统和磨损率分别下降了40%和60%；油润滑时，Al/MoSi2材料的磨损机制主要表现为疲劳点蚀和磨粒磨损。     

Al2O3—TiB2陶瓷刀具材料的高温氧化及其对刀具耐磨性能的影响

研究了Ａｌ２Ｏ３－ＴｉＢ２陶瓷刀具材料在１０００℃下的氧化行为，用ＸＲＤ、ＳＥＭ分析了氧化后的相组成及显微结构。结果表明：Ａｌ２Ｏ３－ＴｉＢ２陶瓷材料在１０００℃空气中氧化增重符合抛物线规律；随ＴｉＢ２含量的增加，该材料的抗氧化能力下降。     

Sliding wear behavior of salt bath nitrocarburized medium carbon steel

Salt bath nitrocarburizing is a well-known thermochemical diffusion process for enhancing the tribological and corrosion properties of ferrous components. The current work describes the role of a compound layer developed during nitrocarburizing, both in the ferritic and austenitic regimes of Fe-N-C system, on the sliding wear behavior of a medium carbon steel. The wear behavior of the nitrocarburized steel discs was assessed by the pin-on-disc tests (ASTM G 99-99) under different normal loads running against a hardened SAE52100 pin. It was observed that the compound layer on the surface not only controlled the wear rate but also resisted the adhesive wear/transfer of material from pin to disc, aside from providing low-friction coefficients.     

氧化铝纤维增强铝基复合材料热磨损研究EI

采用挤压铸造法制备了Al_2O_3纤维和Al_2O_3·SiO_2纤维增强铝基复合材料,在室温及200～400℃下进行了磨损性能试验。结果表明,复合材料的热磨损性能受纤维取向、纤维成分、基体成分及对磨材料硬度所影响,纤维的加入明显提高了铝基合金的耐磨性。     

Tribology of nitriding layer, TiN coatings and their complex on AISI D2 steel

The sliding wear and impact wear resistances of D2 steel with nitriding layer, PVD titanium nitride coating and their duplex treatment were investigated. The experimental results suggest that the duplex treatment has the best sliding and impact wear resistances under experimental conditions. And the wear resistance of PVD titanium nitride is better than that of nitriding. The impact wear resistance and wear mechanism of all three surface layers remain unchanged under impact load of 0.2 J or 1 J. All samples end with the same symptom of flaking.     

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.