High-Velocity Projectile Impact Behaviour of Friction Stir Welded AA7075 Thick Plates

In this study, high velocity impact behaviour of friction stir welded AA7075-T651 25 mm thick plates were investigated using a 7.62 mm × 51 mm lead core and 7.62 mm × 39 mm steel core projectiles. Prior to ballistic trails, mechanical and metallurgical properties of friction stir welded AA 7075-T651 25 mm thick plates were studied. Microstructural and hardness studies revealed that friction stir welds constituted three distinct regions namely Weld Nugget(WN), Thermo-Mechanically Affected Zone(TMAZ) and Heat Affected Zone(HAZ). Base Material(BM) and all three weld regions were ballistically tested as per military standard NIJ.0108.01 using lead and steel core bullets at maximum permissible velocities of 830 ± 20 and 700 ± 30 m/s, respectively. It has been found that base material(AA7075-T651)and all three weld regions of 25 mm thick plates were able to resist perforation by both types of projectiles used. However depth of penetration has been found to increase from BM to WN, HAZ and TMAZ for both types of projectiles. In all cases steel core projectiles caused higher depth of penetration compared to those caused by lead core projectiles. TMAZs of the friction stir welds were found to be the weakest zone. The fracture that occurred in the base material was spall fragmentation indicating brittle failure, whereas all zones of friction stir welded AA7075-T651 targets with a front petalling, indicating ductile failure. The post-ballistic tested samples showed no significant change in the microstructure of the BM and WN. On the other hand, TMAZ and HAZ showed severe grain deformation in the direction of projectile penetration, and the formation of adiabatic shear bands(ASB). This work showed that 25 mm thick friction stir welded AA7075-T651 joints responded well to ballistic impact loads, making them a good choice for light combat vehicles.

Latency Minimization Using an Adaptive Load Balancing Technique in Microservices Applications

Advancements in cloud computing and virtualization technologies have revolutionized Enterprise Application Development with innovative ways to design and develop complex systems.Microservices Architecture is one of the recent techniques in which Enterprise Systems can be developed as fine-grained smaller components and deployed independently.This methodology brings numerous benefits like scalability,resilience,flexibility in development,faster time to market,etc.and the advantages;Microservices bring some challenges too.Multiple microservices need to be invoked one by one as a chain.In most applications,more than one chain of microservices runs in parallel to complete a particular requirement To complete a user’s request.It results in competition for resources and the need for more inter-service communication among the services,which increases the overall latency of the application.A new approach has been proposed in this paper to handle a complex chain of microservices and reduce the latency of user requests.A machine learning technique is followed to predict the weighting time of different types of requests.The communication time among services distributed among different physical machines are estimated based on that and obtained insights are applied to an algorithm to calculate their priorities dynamically and select suitable service instances to minimize the latency based on the shortest queue waiting time.Experiments were done for both interactive as well as non interactive workloads to test the effectiveness of the solution.The approach has been proved to be very effective in reducing latency in the case of long service chains.

电火花线切割5083Al合金的实验研究和参数优化(英文)

通过实验分析,优化电火花线切割5083 Al合金的工艺参数。基于Taguchi优化法,将脉宽、脉间、峰电流和线张力作为输入参数进行了系列实验。将表面粗糙度和切割速度作为响应,基于信噪比,测定了输入参数对响应的影响。通过Taguchi优化方法,得到了最大切割速度和最小表面粗糙度的最佳加工参数。再采用附加的模型预测可能的加工组合。最后,通过使用Pareto优化法,得到一个简明的工艺参数表。