Effect of Cu doping on the microstructure and mechanical properties of AlTiVN-Cu nanocomposite coatings

Cu phase has been incorporated into hard coatings to form nanocomposite structure, which not only enhanced the hardness but also the toughness due to excellent ductility of copper. In this study, a single Al67Ti33-V-Cu spliced target was used to prepare the AlTiVN-Cu nanocomposite coatings, and the effect of Cu doping on microstructure and mechanical properties of AlTiVN-Cu coatings has been investigated. The results showed that the deposition rate linearly increased from 3.8 to 13.4 nm/min when Cu content increased from 2.6 to 46.7 at.%. AlTiVN-Cu coatings exhibited a Ti-Al-V-N solid-solution phase with strong (111) preferred orientation at low Cu contents below 8.3 at.%. When Cu content increased above 22.6 at.%, Cu atoms grew up into metallic crystallites and strongly suppressed crystal growth of nitride coatings due to repeated nucleation. With increasing Cu content, the microstructure transferred from compact columnar to dense featureless, and then to coarse columnar structure. AlTiVN-Cu(2.6 at.%) coating exhibited a super hardness of 41.1 GPa and an excellent toughness with a high H3/E*2 ratio of 0.24

Influence of lubricious oxides formation on the tribological behavior of Mo-V-Cu-N coatings deposited by HIPIMS

The variations of microstructure, mechanical properties, and oxidation behavior of Mo-V-Cu-N coatings are directly correlated to the chemical compositions, which significantly affects their tribological behavior. The aim of this work was to characterize Mo-V-Cu-N coatings with different chemical compositions deposited by high power impulse magnetron sputtering (HIPIMS) using single Mo-V-Cu segmental target, and to investigate the correlations between the lubricative oxides formed on coating surfaces with the variation of tribological behavior at different temperatures. The oxidation of Mo-V-Cu-N coatings started at 400 °C with the lubrication oxides of Mo-O and Cu-Mo-O were formed, which led to the decrease in coefficients of friction and wear rates of the coatings. It was found that the rapid outward diffusion of Mo and Cu atoms took place preferentially at around the growth defects (e.g. microparticles and pores). The incorporation of V atoms into Mo-Cu-N coatings enhanced the oxidation resistance at temperatures below 400 °C. At 500 °C, all the fcc B1-MoN and VN phases disappeared due to the severe oxidation, and the V2O5 phase was first appeared. Even though a relatively low coefficient of friction was obtained at 500 °C, the wear resistance of Mo-V-Cu-N coatings was decreased due to the severe oxidation and loss of mechanical strength

Influence of pulse frequency on microstructure and mechanical properties of Al-Ti-V-Cu-N coatings deposited by HIPIMS

As an important parameter of HIPIMS, pulse frequency has significant influence on the microstructure and mechanical properties of the deposited coatings, especially for the multi-component coatings deposited by using a spliced target with different metal sputtering yields. In this study, a single Al67Ti33-V-Cu spliced target was designed to prepare Al-Ti-V-Cu-N coatings by using high power impulse magnetron sputtering (HIPIMS). The results showed that the peak target current density decreased from 0.75 to 0.24 A∙cm−2 as the pulse frequency increased, along with the microstructure transferred from dense structure to coarse column structure. The pulse frequency has significant influence on chemical compositions of Al-Ti-V-Cu-N coatings, especially for Cu content increasing from 6.2 to 11.7 at.%. All the coatings exhibited a single solid-solution phase of Ti-Al-V-N, and the preferred orientation changed from (111) to (220) when the pulse frequency increased above 200 Hz. The decrease in peak target current density at high pulse frequencies resulted in a sharp decrease in the coating hardness from 35.2 to 16.4 GPa, whereas the relaxation of compressive residual stress contributed to an improvement in adhesion strength from 43.3 to 79.6 N

Primary Study on Medium and Low Carbon Ferromanganese Production by Blowing CO2-O2 Mixtures in Converter

The production of medium- and low-carbon ferromanganese (M-LCFeMn) using the converter method has not been industrialized to date in China due to the high manganese loss and serious erosion of the furnace lining. To solve the above problems and to improve the refining technology of M-LCFeMn, the introduction of CO2 gas into the traditional converter process is proposed. In this study, the oxidation behavior of C and Mn in various conditions was analyzed by blowing different proportions of CO2-O2 mixed gas into the high-carbon ferromanganese (HCFeMn) melt. The results showed that it is feasible to make M-CFeMn by blowing CO2-O2 mixtures, and the Mn loss can be effectively reduced during the decarburization process. It is considered that when the proportion of CO2 reaches 25%, the mixed gas has the best effect on the decarburization and manganese preservation under current experimental situation. Two hypotheses and corresponding rate formulas of decarburization kinetics by using pure oxygen are put forward, and the effect of CO2 on the kinetics of decarburization was studied according to different hypotheses

Primary Study on Medium and Low Carbon Ferromanganese Production by Blowing CO₂-O₂ Mixtures in Converter

The production of medium- and low-carbon ferromanganese (M-LCFeMn) using the converter method has not been industrialized to date in China due to the high manganese loss and serious erosion of the furnace lining. To solve the above problems and to improve the refining technology of M-LCFeMn, the introduction of CO2 gas into the traditional converter process is proposed. In this study, the oxidation behavior of C and Mn in various conditions was analyzed by blowing different proportions of CO2-O2 mixed gas into the high-carbon ferromanganese (HCFeMn) melt. The results showed that it is feasible to make M-CFeMn by blowing CO2-O2 mixtures, and the Mn loss can be effectively reduced during the decarburization process. It is considered that when the proportion of CO2 reaches 25%, the mixed gas has the best effect on the decarburization and manganese preservation under current experimental situation. Two hypotheses and corresponding rate formulas of decarburization kinetics by using pure oxygen are put forward, and the effect of CO2 on the kinetics of decarburization was studied according to different hypotheses

Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5

Aiming at the problems of low efficiency and poor accuracy in conventional surface defect detection methods for aero-engine components, a surface defect detection model based on an improved YOLOv5 object detection algorithm is proposed in this paper. First, a k-means clustering algorithm was used to recalculate the parameters of the preset anchors to make them match the samples better. Then, an ECA-Net attention mechanism was added at the end of the backbone network to make the model pay more attention to feature extraction from defect areas. Finally, the PANet structure of the neck network was improved through its replacement with BiFPN modules to fully integrate the features of all scales. The results showed that the mAP of the YOLOv5s-KEB model was 98.3%, which was 1.0% higher than the original YOLOv5s model, and the average inference time for a single image was 2.6 ms, which was 10.3% lower than the original model. Moreover, compared with the Faster R-CNN, YOLOv3, YOLOv4 and YOLOv4-tiny object detection algorithms, the YOLOv5s-KEB model has the highest accuracy and the smallest size, which make it very efficient and convenient for practical applications

Psyllaphorura Bagnall 1948

Key to the species of <i>Psyllaphorura</i> Bagnall, 1948 <p>1. Th. II without pso........................................................................................................................................ 2</p> <p> - Th. II with 1+1 pso.............................................................................................................................. <i>obesa</i></p> <p>2. Head with 2+2 anterior pso.......................................................................................................................... 3</p> <p> - Head with 5+5 anterior pso.......................................................................................................... <i>bashkirica</i></p> <p>3. Abd. IV with 1+1 pso.................................................................................................................................. 4</p> <p> - Abd. IV with 2+2 pso.................................................................................................................................. 5 4. Ventral tube with 7+7 setae <i>......................................................................................................... martynovae</i></p> <p> - Ventral tube with 5+5 setae.............................................................................................. <i>jiangsuensis</i> <b>sp. n.</b></p> <p>5. 2+2 cephalic anterior pso placed near to antennal bases............................................................................. 6</p> <p> - 2+2 cephalic anterior pso placed together, medially in antenno-frontal part............................. <i>ryozoyoshii</i></p> <p>6. Dorsal sensory setae distinct, especially on Abd. V, PAO with more than 20 vesicles.............................. 7</p> <p> - Dorsal sensory setae invisible, PAO with 15–18 vesicles <i>................................................................... uenoi</i></p> <p> 7. Unguiculus half as long as inner edge of unguis............................................................................... <i>okafujii</i></p> <p> - Unguiculus longer than inner edge of unguis <i>............................................................................... sensillifera</i></p>Published as part of <i>Yan, Haijuan, Huang, Cheng & Chen, Jian-Xiu, 2007, Psyllaphorura jiangsuensis sp. nov. from China (Collembola: Onychiuridae), pp. 63-68 in Zootaxa 1510 (1)</i> on pages 63-64, DOI: 10.11646/zootaxa.1510.1.5, <a href="http://zenodo.org/record/5087255">http://zenodo.org/record/5087255</a&gt

Psyllaphorura jiangsuensis Yan & Huang & Chen 2007, sp. nov.

Psyllaphorura jiangsuensis sp. nov. Figs 1–12, Tab. 1 Type material. Holotype: female, China, Jiangsu Province, Nanjing: Qixiashan Park, under leaf litter, stones and bricks in deciduous forest; 14-X-1994; collection number C8420, coll. Jian-xiu Chen; paratypes: 3 females, same data as holotype. 1 female, same locality as holotype, 25-V-2005, C9251, coll. Hai-juan Yan & Jun-li Jia; in the collection of the Laboratory of Zoology, Nanjing University, China. Diagnosis. Antennal III sense organ with 4 guard papillae. Postantennal organ composed of 20–22 simple or bilobed vesicles. Furca reduced to 2 mamelons, each with 3 distal setae. Pseudocellar formula dorsally as 20/000/00013, pseudocelli absent on ventral side and subcoxae. Cephalic chaetotaxy without seta d 0. Unguiculus basal lamella absent. Ventral tube with 5+5 setae. Anal spines set on very distinct papillae. Description. Maximum body length: females 1.52 mm. Habitus typical for the genus: oval, plump, dorsoventrally flattened with very short abdominal segment VI (Fig. 1). Body color milky white. Integument coarse. Granules up to 5 µm in diameter dorsally on body, 3 µm on antennae and much smaller ventrally on body and antennae. Antennal base well marked with very fine granules. Body segments distinctly defined. Thoracic terga I–III and abdominal terga I–III with two rows of smaller granules along medial line. Pseudocelli mostly with margin poorly demarcated and hardly distinguished, dorsally arranged as 20/000/ 00013, absent on ventral side and subcoxae. Parapseudocelli invisible. Thoracic terga II–III and abdominal terga I–IV with1+1 pseudopores (Fig. 1). Body setae: macrosetae and mesosetae thick and apically rounded, microsetae short and apically pointed. Sensory setae (s) thick and short (Fig. 5), microsensillum (ms) tiny. S formula dorsally as 1/011/11111 (Fig. 1). Head with seta d 0 absent. 2+2 anterior cephalic pseudocelli, located outside area of fine granulation at antennal base (Figs. 1 & 6). Posterior pseudocelli absent. Postantennal organ composed of 20–22 simple or bilobed vesicles perpendicular to the long axis of the organ. Antennae cylindrical, 0.6–0.7 times as long as head. Antennal segments III and IV fused into one club. Length ratio of antennal segments I–IV as 1: 1.0–1.2: 1.4–2.0: 2.1–3.6. Antennal segment IV with 6–7 distinct short and thick sensory setae, 1 small subapical peg in shallow pit, and 1 baso-lateral microsensillum. Ant III organ with 4 papillae, 5 guard setae, 2 small rods and 2 sensory clubs; club smooth, curved, each with one rib. Lateral microsensillum on antennal segment III slightly behind sense organ (Fig. 3). Other antennal setae all acuminate. Labium of AC type (Fjellberg, 1999), labial proximal setae 6. Basomedian field with 4 setae (E, F, G, and f), basolateral–with 5 (b, c, d, e, e') (D' Haese, 2003) (Fig. 4). Postlabial setae 3+3 present along ventral groove. Dorsal thoracic chaetotaxy as in Fig. 1. Thoracic terga II and III each with 1+1 lateral microsensillum. Tibiotarsal chaetotaxy symmetrical, with 11 setae in whorl 1 (distal whorl), 7 & 1 respectively in whorl 2 and 3 (Fig. 8). Unguis without teeth. Unguiculus narrow and pointed, without basal inner lamella, apical filament 0.8–0.9 as long as inner edge of unguis. All setae on legs acuminate. Abdominal terga I–IV without axial setae. Abdominal tergum V with m 0 and p 0 setae. Abdominal tergum VI with one unpaired seta m 0. Anal spines short, about 0.3–0.4 times length of inner edge of hind unguis, set on very distinct papillae (Fig. 1). Ventral tube with 5+5 distal setae, without setae at base (Fig. 10). Furcula as 2 mamelons, each bearing 3 setae (Fig. 11). Female genital plate with 26 setae (only seen in one paratype). Anal valves with numerous acuminate setae, each lateral valve with a 0, 2 a 1 setae, upper valve with a 0, 2 a 1, 2 a 2, 2 b 1, 2b 2, c 0, and 2 c 1 setae (Fig. 12). Etymology. The new species is named after the type locality: Jiangsu Province. Discussion. Habitus, coarse granulation, absence of posterior cephalic pseudocelli and the structure of furcal remnant show that the new species belongs to the genus Psyllaphorura. Within the genus, the new species distinctly resembles the South Siberian P. martynovae in the pseudocellar formula and the number of papillae on antennal III sense organ. However, it differs from the latter by the characters shown in table 1.Published as part of Yan, Haijuan, Huang, Cheng & Chen, Jian-Xiu, 2007, Psyllaphorura jiangsuensis sp. nov. from China (Collembola: Onychiuridae), pp. 63-68 in Zootaxa 1510 (1) on pages 64-67, DOI: 10.11646/zootaxa.1510.1.5, http://zenodo.org/record/508725

A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks

The centrality plays an important role in many community-detection algorithms, which depend on various kinds of centralities to identify seed vertices of communities first and then expand each of communities based on the seeds to get the resulting community structure. The traditional algorithms always use a single centrality measure to recognize seed vertices from the network, but each centrality measure has both pros and cons when being used in this circumstance; hence seed vertices identified using a single centrality measure might not be the best ones. In this paper, we propose a framework which integrates advantages of various centrality measures to identify the seed vertices from the network based on the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multiattribute decision-making technology. We take each of the centrality measures involved as an attribute, rank vertices according to the scores which are calculated for them using TOPSIS, and then take vertices with top ranks as the seeds. To put this framework into practice, we concretize it in this paper by considering four centrality measures as attributes to identify the seed vertices of communities first, then expanding communities by iteratively inserting one unclassified vertex into the community to which its most similar neighbor belongs, and the similarity between them is the largest among all pairs of vertices. After that, we obtain the initial community structure. However, the amount of communities might be much more than they should be, and some communities might be too small to make sense. Therefore, we finally consider a postprocessing procedure to merge some initial communities into larger ones to acquire the resulting community structure. To test the effectiveness of the proposed framework and method, we have performed extensive experiments on both some synthetic networks and some real-world networks; the experimental results show that the proposed method can get better results, and the quality of the detected community structure is much higher than those of competitors

A Node Similarity and Community Link Strength-Based Community Discovery Algorithm

Community structure is one of the common characteristics of complex networks. In the practical work, we have noted that every node and its most similar node tend to be assigned to the same community and that two communities are often merged together if there exist relatively more edges between them. Inspired by these observations, we present a community-detection method named NSCLS in this paper. Firstly, we calculate the similarities between any node and its first- and second-order neighbors in a novel way and then extract the initial communities from the network by allocating every node and its most similar node to the same community. In this procedure, some nodes located at the community boundaries might be classified in the incorrect communities. To make a redemption, we adjust their community affiliations by reclassifying each of them into the community in which most of its neighbors have been. After that, there might exist relatively larger number of edges between some communities. Therefore, we consider to merge such communities to improve the quality of the final community structure further. To this end, we calculate the link strength between communities and merge some densely connected communities based on this index. We evaluate NSCLS on both some synthetic networks and some real-world networks and show that it can detect high-quality community structures from various networks, and its results are much better than the counterparts of comparison algorithms