Accelerated electrospark deposition and the wear behavior of coatings

Electrospark deposition (ESD) is a coating process that is featured by low heat input to the substrate. Low coating efficiency and other limitations influence its wider application. The present paper introduces newly designed ESD equipment, by which a higher coating rate can be reached. The relationship among coating thickness, surface roughness, and process parameters such as pulse energy, pulse frequency, and deposition time are presented. Electrospark deposition coating by the new equipment on AISI 1045 steel (with WC-8% Co as electrode) increases the wear resistance by 5 to 8 times. The micromechanism is investigated by scanning electron microscopy observation.     

用双层辉光等离子法在钛表面制备的Ti-Pd合金层性能研究

采用双层辉光等离子冶金技术在纯钛表面制备了Ti—Pd合金层。其深度大约为90μm，Pd含量呈梯度变化，并出现了TiPd3，TiPd2，Ti2Pd3，Ti3Pd5，TiPd，Ti4Pd等6种化合物相和Pd相。合金层在100℃的NaCl饱和溶液＋HCl溶液以及40℃的8．6％H2SO4溶液中的耐缝隙腐蚀性能优于Ti0．2Pd合金；在室温80％H2SO4的溶液中，腐蚀速率仅为0．682mm／a，是Ti0．2Pd合金的18．2％：在室温30％HCl的溶液中，表面Ti—Pd的腐蚀速率仅为0．004mm／a，是Ti0．2Pd合金的12．5％。     

时效对接触线用Cu-Ag-Cr合金性能的影响

分析了固溶温度、时效及时效前冷变形量对Cu - 0 1 %Ag - 0 1 %Cr接触线用合金性能的影响 ,结果表明 :经 870℃固溶、 40 %～ 5 0 %冷变形及 480℃× 1 5h时效处理后 ,合金可获得良好的综合性能。     

医用钴基合金的组织结构及耐腐蚀性能

以锻造CoNiCrMo，CoCrMo和CoCrMoC台会为研究对象，通过金相观察，XRD分析和耐腐蚀试验，研究了3组合金在不同热处理条件下的显微结构，相成分及在Hi同溶液中的耐腐蚀性能。结果表明：2组锻造合金的再结晶温度都是1000℃，且晶粒尺寸随着固溶温度升高和时效时间延长而逐渐变大。3组试样在NaCl溶液中为钴基合金典型的阳极极化曲线；在Hanks’溶液中均有一个二次钝化行为，且锻造CoNiCrMo合金的二次钝化区域较窄，而其他2组合金则较宽；在含柠檬酸三钠的溶液中过钝化电位降低，柠檬酸盐的存在降低了含金的耐腐蚀性能。热处理对合金的耐腐蚀性能影响不大。     

Ni-Cr-Fe-Si-B合金涂层及铝青铜基体熔合区组织的研究

采用钨极氩弧焊方法在铝青铜基体上施焊了Ni—Cr-Fe-Si—B合金涂层，并运用金相显微镜、扫描电子显微镜及能谱分析了喷涂层、熔合区及热影响区的组织形貌和成分分布。结果表明，喷焊时，在涂层与基体界面两侧各组元均发生了明显的扩散，Cu、Ni组元间可相互取代，基体融合区组织的基本组成仍为α (α γ2)，但同时也形成了Cu2FeAl7、NiAl3等新相，且融合区组织细小，涂层与基体间为冶金态结合。     

保温条件下过共晶Al-Si合金半固态浆料的组织演变

研究了保温时间和保温温度对电磁搅拌ZL117合金半固态浆料稳定性的影响.试验结果表明：半固态浆料中初生Si的颗粒直径随着保温时间的延长而逐渐增大,但在前15 min内增长速度比较缓慢,15 min后增长较快;随着保温温度的升高而缓慢增大;初生Si的形状系数随着保温时间的延长而变得越来越大,但也在前15 min内变化较为缓慢,15 min后变大速度突然加快;初生Si的形状系数在620 ℃下保温时最小,在610～620 ℃之间随着保温温度的升高而缓慢变小;在620～650 ℃之间随着保温温度的升高而逐渐增加.     

Dual–primal proximal point algorithms for extended convex programming

This paper presents a class of dual–primal proximal point algorithms (PPAs) for extended convex programming with linear constraints. By choosing appropriate proximal regularization matrices, the application of the general PPA to the equivalent variational inequality of the extended convex programming with linear constraints can result in easy proximal subproblems. In theory, the sequence generated by the general PPA may fail to converge since the proximal regularization matrix is asymmetric sometimes. So we construct descent directions derived from the solution obtained by the general PPA. Different step lengths and descent directions are chosen with the negligible additional computational load. The global convergence of the new algorithms is proved easily based on the fact that the sequences generated are Fejér monotone. Furthermore, we provide a simple proof for the O(1/t) convergence rate of these algorithms.