PSG College of Technology

About: PSG College of Technology is a based out in . It is known for research contribution in the topics: Machining & Thin film. The organization has 3174 authors who have published 3575 publications receiving 40690 citations.

Linear Scheduling Strategy for Resource Allocation in Cloud Environment

Abstract: Cloud computing technology virtualizes and offers many services across the network. It mainly aims at scalability, availability, throughput, and resource utilization. Emerging techniques focus on scalability and availability. However, cloud computing must be advanced to focus on resource utilization and resource management. The cloud environment, embedded with the nimbus and cumulus services will contribute more in making the responsibility of resource utilization in Cloud Computing. Considering the processing time, resource utilization based on CPU usage, memory usage and throughput, the cloud environment with the service node to control all clients request, could provide maximum service to all clients. Scheduling the resource and tasks separately involves more waiting time and response time. A scheduling algorithm named as Linear Scheduling for Tasks and Resources (LSTR) is designed, which performs tasks and resources scheduling respectively. Here, the combination of Nimbus and Cumulus services are imported to a server node to establish the IaaS cloud environment and KVM/Xen virtualization along with LSTR scheduling is used to allocate resources which maximize the system throughput and resource utilization.

Prediction of surface roughness of end milling operation using genetic algorithm

Abstract: In the present study, the predictive model is developed to observe the effect of radial rake angle on the end milling cutting tool by considering the following machining parameters: spindle speed, feed rate, axial depth of cut, and radial depth of cut. By referring to the real machining case study, the second-order mathematical models have been developed using response surface methodology (RSM). A number of machining experiments based on statistical five-level full factorial design of experiments are carried out in order to collect surface roughness values. The direct and interaction effects of the machining parameter with surface roughness are analyzed using Design Expert software. The optimal surface roughness value can be attained within the specified limits by using RSM. The genetic algorithm (GA) model is trained and tested in MATLAB to find the optimum cutting parameters leading to minimum surface roughness. The GA recommends 0.25 μm as the best minimum predicted surface roughness value. The confirmatory test shows the predicted values which were found to be in good agreement with observed values.

Detection of Sinkhole Attack in Wireless Sensor Networks Using Message Digest Algorithms

Abstract: Wireless sensor networks (WSN) are susceptible to routing attacks. The main goal of the protocol is to detect the exact sink hole using the one-way hash chains. In the proposed method destination/sink detects the attack only when the digest obtained from the trustable forward path and the digest obtained through the trustable node to the destination are different. It also ensures the data integrity of the messages transferred using the trustable path. The algorithm is also robust to deal with cooperative malicious nodes that attempt to hide the real intruder. The functionality of the proposed algorithm is tested in MAT lab. The performance of the proposed algorithm is evaluated through simulations, and that confirmed the accuracy of the algorithm in terms of Success rate, false positive rate and false negative rate.

Ageing of forged superaustenitic stainless steel: Precipitate phases and mechanical properties

Abstract: Cast superaustenitic stainless steel (SASS), of composition 19 wt% Cr, 20 wt% Ni, 7.5 wt% Mo and 0.37 wt% N, is hot forged at 1200 °C. The forging was then solutionized at 1250 °C and aged for 1 h and 10 h at different temperatures in the range of 500–1000 °C. Effect of these treatments on (i) hardness and (ii) fracture toughness based on impact energy is reported. Chi is formed from low temperatures up to 800 °C, and sigma at temperatures above 900 °C. A model is proposed to illustrate the dissolution of chi and sigma in austenite at elevated temperatures. Compared with chi, sigma contains more Mo. Toughness decreased with increasing amounts of chi and sigma precipitates. However, in the temperature range of 850–950 °C, low toughness was observed for relatively a short ageing time, although virtually the volume of sigma phase is very low. This is attributed to the presence of incoherent sigma in austenite matrix. Fractographs of the impact-tested samples indicated an increased tendency for brittle fracture with increasing ageing temperatures (increase in sigma content). Thermodynamic calculations substantiated (i) EDS results of composition of secondary phases present in the aged SASS and (ii) the proposed model. From these studies it is concluded that sigma is enriched in Mo at 900 °C and can be as detrimental to toughness of SASS as chi or carbides and nitrides.