Showing papers by "PSG College of Technology published in 2012"

Optimization of machining parameters at high speed drilling of carbon fiber reinforced plastic (CFRP) laminates

Abstract: High speed machining is now acknowledged to be one of the key manufacturing technologies to ensure high productivity and throughput. Drilling of CFRP, though a challenging task, is being performed successfully at low spindle speeds. However high speed drilling in CFRP thin laminates has not been explored much. This paper reports an experimental investigation of a full factorial design performed on thin CFRP laminates using K20 carbide drill by varying the drilling parameters such as spindle speed and feed rate to determine optimum cutting conditions. The hole quality parameters analyzed include hole diameter, circularity, peel-up delamination and push-out delamination. Analysis of variance (ANOVA) was carried out for hole quality parameters and their contribution rates were determined. Genetic Algorithm (GA) methodology was used in the multiple objective optimization (using MATLAB R2010a software) to find the optimum cutting conditions for defect free drilling. Tool life of the K20 carbide drill was predicted at optimized cutting speed and feed.

Influence of machining parameters and new nano-coated tool on drilling performance of CFRP/Aluminium sandwich

Abstract: Drilling and fastening of hybrid materials in one-shot operation reduces cycle time of assembly of aerospace structures. One of the most common problems encountered in automatic drilling and riveting of multimaterial is that the continuous chips curl up on the body of the tool. Drilling of carbon fiber reinforced plastic (CFRP) is manageable, but when the minute drill hits the aluminium (Al) or titanium (Ti), the hot and continuous chips produced during machining considerably damage the CFRP hole. This study aims to solve this problem by employing nano-coated drills on multimaterial made of CFRP and aluminium alloy. The influence of cutting parameters on the quality of the holes, chip formation and tool wear were also analyzed. Two types of tungsten carbide drills were used for the present study, one with nano-coating and the other, without nano coating. The experimental results indicated that the shape and the size of the chips are strongly influenced by feed rate. The thrust force generated during drilling of the composite plate with coated drills was 10–15% lesser when compared to that generated during drilling with uncoated drills; similarly, the thrust force in the aluminium alloy was 50% lesser with coated drills when compared to thrust force generated without coated drills. Thus, the use of nano-coated drills significantly reduced the surface roughness and thrust force when compared with uncoated tools.

Predicting the depth of penetration and weld bead width from the infra red thermal image of the weld pool using artificial neural network modeling

Abstract: It is necessary to estimate the weld bead width and depth of penetration using suitable sensors during welding to monitor weld quality. Among the vision sensors, infra red sensing is the natural choice for monitoring welding processes as welding is inherently a thermal processing method. An attempt has been made to estimate the weld bead width and depth of penetration from the infra red thermal image of the weld pool using artificial neural network models during A-TIG welding of 3 mm thick type 316 LN stainless steel plates. Real time infra red images were captured using IR camera for the entire weld length during A-TIG welding at various current values. The image features such as length and width of the hot spot, peak temperature, and other features using line scan analysis are extracted using image processing techniques corresponding to particular locations of the weld joint. These parameters along with their respective current values are used as inputs while the measured weld bead width and depth of penetration are used as output of the neural network models. Accurate ANN models predicting weld bead width (9-11-1) and depth of penetration (9-9-1) have been developed. The correlation coefficient values obtained were 0.98862 and 0.99184 between the measured and predicted values of weld bead width and depth of penetration respectively.

Dry sliding wear behaviour of aluminium/alumina/graphite hybrid metal matrix composites

Abstract: Purpose – Recent trends in material science show a considerable interest in the manufacturing of metal matrix composites to meet the stringent demands of lightweight, high strength and corrosion resistance. Aluminium is the popular matrix metal currently in vogue that can be reinforced with ceramic materials such as particulates to meet the desired property. The purpose of this paper is to fabricate hybrid metal matrix composites to improve the dry sliding wear resistance and to study of the effect of sliding speed, load and reinforcement (alumina and graphite) on wear properties, as well as its contact friction.Design/methodology/approach – The present study addresses the dry sliding wear behaviour of Al‐Si10Mg alloy reinforced with 3, 6 and 9 wt% of alumina along with 3 wt% of graphite. Stir casting method was used to fabricate the composites. Mechanical properties such as hardness and tensile strength have been evaluated. A pin‐on‐disc wear test apparatus was used to evaluate the wear rate and coeffici...

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.

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.

HTTP botnet detection using adaptive learning rate multilayer feed-forward neural network

Abstract: Botnets have become a rampant platform for malicious attacks, which poses a significant threat to internet security. The recent botnets have begun using common protocols such as HTTP which makes it even harder to distinguish their communication patterns. Most of the HTTP bot communications are based on TCP connections. In this work some TCP related features have been identified for the detection of HTTP botnets. With these features a Multi-Layer Feed Forward Neural Network training model using Bold Driver Back-propagation learning algorithm is created. The algorithm has the advantage of dynamically changing the learning rate parameter during weight updation process. Using this approach, Spyeye and Zeus botnets are efficiently identified. A comparison of the actively trained neural network model with a C4.5 Decision Tree, Random Forest and Radial Basis Function indicated that the actively learned neural network model has better identification accuracy with less false positives.

Influence of nano titanium dioxide finish, prepared by sol-gel technique, on the ultraviolet protection, antimicrobial, and self-cleaning characteristics of cotton fabrics

Abstract: The performance of ultraviolet (UV) protection, antimicrobial activity, and self-cleaning characteristics of nano titanium dioxide (TiO2) with acrylic binder were assessed on the cotton fabric usin...

Stream cipher for binary image encryption using Ant Colony Optimization based key generation

Abstract: Encryption of binary images is essential since it is vulnerable to eavesdropping in wired and wireless networks. The security of data becomes important since the communications over open network occur frequently. This paper focuses on encryption of binary image using a stream cipher method. In this paper we propose an Ant Colony Optimization (ACO) based approach of generating keys for encryption. The binary image is represented in a text form and encrypted using a stream cipher method. A novel technique termed Ant Colony Optimization Key Generation Binary Image Encryption (AKGBE) algorithm employs a character code table for encoding the keys and the plain text representing the binary image. The main advantage of this approach is that it reduces the number of keys to be stored and distributed. Experimental results demonstrating AKGBE's encrypting binary images of different sizes and the comparison of its performance with other stream cipher methods are presented.

Student's Expectation, Perception and Satisfaction towards the Management Educational Institutions

Abstract: This study assesses management education student's expectation, perception and satisfaction of services experienced across four categories of institutions in Coimbatore. Institution quality factors were captured using structured questionnaire across six dimensions namely, location, academics, infrastructure, image, cost and personnel and overall satisfaction. A significant difference was found between the perception of students across four categories of institutions in all six dimensions of institution quality factors. All five factors except cost significantly influence the overall satisfaction of students towards the institution.

Sensitisation of SMEs towards the implementation of Lean Six Sigma – an initialisation in a cylinder frames manufacturing Indian SME

Abstract: This article suggests the adoption of a model on Lean Six Sigma for successfully implementing it in small and medium engineering enterprises (SMEs). This model is given the name Deficiency Overcoming Lean Anchorage Define Measure Analyse Improve Control Stabilise (DOLADMAICS). The DOLADMAICS model has been designed to lift up an SME through the implementation of Lean Six Sigma in five levels. The implementation study on first level of DOLADMAICS model conducted in an Indian SME, manufacturing a component called ‘cylinder frames’, has been reported in this article. After conducting this implementation study, it was found that the first level of DOLADMAICS model would act as a catalyst and guide for sensitising the management of SMEs to successfully implement Lean Six Sigma.

Global views on integrating Six Sigma and ISO 9001 certification

Abstract: During the past three decades, ISO 9001 certifications have facilitated contemporary organisations to systematically achieve continual quality improvement. In the same period, Six Sigma has propelled organisations towards achieving zero defect manufacturing. Though these objectives are closely coinciding, the synergy of implementing ISO 9001 standard and Six Sigma has been eluding the contemporary organisations. This situation creates a need to globally view the development that has been occurring in the direction of integrating ISO 9001 certification and Six Sigma. This study reports a literature review conducted in this context. The result of this literature review indicated that, very little work on integrating Six Sigma and ISO 9001 standard has so far been carried out. Hence, it is concluded that the recent most version, namely ISO 9001:2008 standard is to be amended with the ideals of Six Sigma. In order to carry out this task, a roadmap is contributed in this paper.

Communities of Practice: An Influencing Factor for Effective Knowledge Transfer in Organizations

Abstract: For managing their intellectual capital and also to utilize the ‘knowledge’ more efficiently, a number of organizations have introduced Knowledge Management (KM) systems. Though explicit knowledge capture and transfer was quite possible, tacit knowledge was still a challenge for organization experts to capture and transfer. The concept of Communities of Practice (CoP) in KM is increasingly becoming popular to enhance social interactions. CoP refers to informal groups of people bound together by a common purpose. In these communities, members are provided opportunity to share their best practices, which are commonly implicit in nature. However, knowledge sharing in CoP has not been fully researched yet. The purpose of this paper is to review the relationship between KM and CoP and examine how it can influence the knowledge sharing process in organizations. This study proposes a critical reading of the studies available on the topic, with the purpose to identify the main elements and methods influencing the transfer of knowledge. It reviews the emerging concept of ‘CoP’ as a knowledge transfer method for sharing knowledge which is commonly implicit in nature. This paper presents that the concept of CoP can foster joint learning that involves tacit and codified knowledge. Members of a CoP share a concern or a passion for something they do and learn how to do it better as they interact regularly. The existence of common knowledge and a shared system of values makes sharing tacit knowledge easier in CoP, as group members have insights into the implicit assumptions and values embedded in each other’s knowledge. An empirical work in managing CoP and in identifying the knowledge-sharing methods represents an interesting challenge for further research in the area.

Crowd Sourcing a New Paradigm for Interactome Driven Drug Target Identification in Mycobacterium tuberculosis

Abstract: A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative ‘Connect to Decode’ (C2D) to generate the first and largest manually curated interactome of Mtb termed ‘interactome pathway’ (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

An efficient FPGA implementation of MRI image filtering and tumor characterization using Xilinx system generator

Abstract: This paper presents an efficient architecture for various image filtering algorithms and tumor characterization using Xilinx System Generator (XSG). This architecture offers an alternative through a graphical user interface that combines MATLAB, Simulink and XSG and explores important aspects concerned to hardware implementation. Performance of this architecture implemented in SPARTAN-3E Starter kit (XC3S500E-FG320) exceeds those of similar or greater resources architectures. The proposed architecture reduces the resources available on target device by 50%.

An enhanced ACO algorithm to select features for text categorization and its parallelization

Abstract: Feature selection is an indispensable preprocessing step for effective analysis of high dimensional data. It removes irrelevant features, improves the predictive accuracy and increases the comprehensibility of the model constructed by the classifiers sensitive to features. Finding an optimal feature subset for a problem in an outsized domain becomes intractable and many such feature selection problems have been shown to be NP-hard. Optimization algorithms are frequently designed for NP-hard problems to find nearly optimal solutions with a practical time complexity. This paper formulates the text feature selection problem as a combinatorial problem and proposes an Ant Colony Optimization (ACO) algorithm to find the nearly optimal solution for the same. It differs from the earlier algorithm by Aghdam et al. by including a heuristic function based on statistics and a local search. The algorithm aims at determining a solution that includes 'n' distinct features for each category. Optimization algorithms based on wrapper models show better results but the processes involved in them are time intensive. The availability of parallel architectures as a cluster of machines connected through fast Ethernet has increased the interest on parallelization of algorithms. The proposed ACO algorithm was parallelized and demonstrated with a cluster formed with a maximum of six machines. Documents from 20 newsgroup benchmark dataset were used for experimentation. Features selected by the proposed algorithm were evaluated using Naive bayes classifier and compared with the standard feature selection techniques. It was observed that the performance of the classifier had been improved with the features selected by the enhanced ACO and local search. Error of the classifier decreases over iterations and it was observed that the number of positive features increases with the number of iterations.

Analysis of Parameters Influencing Wear and Frictional Behavior of Aluminum–Fly Ash Composites

Abstract: Aluminum alloy (Al/3.25Cu/8.5Si) composites reinforced with fly ash particles of three different size ranges (53–75, 75–103, and 103–150 μm) in 3, 6, 9, and 12 weight percentages were fabricated using a stir-casting technique. Pin-on-disc wear tests were conducted with 20, 30, and 40 N loads and sliding speeds of 2, 3, and 4 m/s for a constant time period of 10 min. A mathematical model was developed to predict the wear volume and coefficient of friction of the composites. An analysis of variance (ANOVA) technique was applied to check the validity of the developed model. Student's t-test was utilized to determine the significance of factors. Composites reinforced with coarse fly ash particles exhibit superior wear resistance to those reinforced with fine fly ash particles. A scanning electron microscope (SEM) study of the worn surfaces of the pins was performed to confirm the results of the model.

Enhancement of wrinkle recovery angle of cotton fabric using citric acid cross-linking agent with nano-TiO2 as a co-catalyst:

Abstract: In this research, the non-formaldehyde wrinkle-resistant treatment of cotton fabrics has been investigated using the citric acid (CA) as a cross-linking agent and sodium hypophosphite (SHP) as a catalyst together with nano-titanium dioxide (nano-TiO2) as a co-catalyst compound. The effect of the changes in the concentrations of CA, SHP, and nano-TiO2 on the wrinkle recovery angle (WRA), tensile strength, tearing strength, whiteness index, and flexural rigidity of cotton fabrics was studied using Box–Behnken design. It was found that the addition of nano-TiO2 could enhance the wrinkle resistance and decrease the flexural rigidity of the cotton fabric with little effect on the whiteness index, and tearing and tensile strengths of the treated fabric. The developed empirical models are found to be in good correlation with the selected variables (r2 ≥ 0.8). From this study, it was concluded that 10% CA, 10% sodium dihydrogen phosphate, and 0.1% nano-TiO2 were the optimum concentrations required to enhance the ...

Flow index based line balancing: a tool to improve the leanness of assembly line design

Abstract: This paper presents an effective approach for balancing assembly operations with criteria to meet takt time by introducing a new parameter referred to as flow index. The proposed approach consists of a two stage heuristic procedure based on the concepts of COMSOAL (computer method of sequencing operations for assembly lines) algorithm which, while attempting to minimise the number of workstations also smoothen the workload among the workstations with lean perspective. The workload smoothness among workstations is achieved by minimising the proposed flow index value. The effectiveness of the proposed approach is demonstrated by comparing the results obtained with those of other popular line balancing heuristics. The experiments are conducted for industrial as well as other benchmark data sets taken from the literature. The computational results show that the proposed approach performs well on all data sets. Tests on problems indicate that the proposed procedure can be used effectively to smoothen the workl...

Security in cognitive radio networks - a survey

Abstract: Cognitive radio is a new technology which compliments the wireless devices by improving the efficiency, speed and reliability. There is always a huge demand for the spectrum usage as the availability of the radio spectrum is limited. Cognitive radio technology is seen as a potential solution to the efficient utilization of available spectrum by the unlicensed legitimate users. One of the major threats of cognitive radio network is the Primary User Emulation attack. In this paper a survey of Cognitive Radio techniques with its IEEE 802.22 standard, various defensive methods against PUE attack, primary signal detection methods and the features of SpiderRadio, a cognitive radio device are presented.

Metaheuristic approach for the multi-depot vehicle routing problem

Abstract: Distribution logistics comprises all activities related to the provision of finished products and merchandise to a customer. The focal point of distribution logistics is the shipment of goods from the manufacturer to the consumer. The products can be delivered to a customer directly either from the production facility or from the trader's stock located close to the production site or, probably, via additional regional distribution warehouses. These kinds of distribution logistics are mathematically represented as a vehicle routing problem VRP, a well-known nondeterministic polynomial time NP-hard problem of operations research. VRP is more suited for applications having one warehouse. In reality, however, many companies and industries possess more than one distribution warehouse. These kinds of problems can be solved with an extension of VRP called multi-depot VRP MDVRP. MDVRP is an NP-hard and combinatorial optimization problem. MDVRP is an important and challenging problem in logistics management. It can be solved using a search algorithm or metaheuristic and can be viewed as searching for the best element in a set of discrete items. In this article, cluster first and route second methodology is adapted and metaheuristics genetic algorithms GA and particle swarm optimization PSO are used to solve MDVRP. A hybrid particle swarm optimization HPSO for solving MDVRP is also proposed. In HPSO, the initial particles are generated based on the k-means clustering and nearest neighbor heuristic NNH. The particles are decoded into clusters and multiple routes are generated within the clusters. The 2-opt local search heuristic is used for optimizing the routes obtained; then the results are compared with GA and PSO for randomly generated problem instances. The home delivery pharmacy program and waste-collection problem are considered as case studies in this paper. The algorithm is implemented using MATLAB 7.0.1.

Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Models for Predicting the Weld Bead Width and Depth of Penetration from the Infrared Thermal Image of the Weld Pool

Abstract: Type 316 LN stainless steel is the major structural material used in the construction of nuclear reactors. Activated flux tungsten inert gas (A-TIG) welding has been developed to increase the depth of penetration because the depth of penetration achievable in single-pass TIG welding is limited. Real-time monitoring and control of weld processes is gaining importance because of the requirement of remoter welding process technologies. Hence, it is essential to develop computational methodologies based on an adaptive neuro fuzzy inference system (ANFIS) or artificial neural network (ANN) for predicting and controlling the depth of penetration and weld bead width during A-TIG welding of type 316 LN stainless steel. In the current work, A-TIG welding experiments have been carried out on 6-mm-thick plates of 316 LN stainless steel by varying the welding current. During welding, infrared (IR) thermal images of the weld pool have been acquired in real time, and the features have been extracted from the IR thermal images of the weld pool. The welding current values, along with the extracted features such as length, width of the hot spot, thermal area determined from the Gaussian fit, and thermal bead width computed from the first derivative curve were used as inputs, whereas the measured depth of penetration and weld bead width were used as output of the respective models. Accurate ANFIS models have been developed for predicting the depth of penetration and the weld bead width during TIG welding of 6-mm-thick 316 LN stainless steel plates. A good correlation between the measured and predicted values of weld bead width and depth of penetration were observed in the developed models. The performance of the ANFIS models are compared with that of the ANN models.

A novel framework for applying multiobjective GA and PSO based approaches for simultaneous area, delay, and power optimization in high level synthesis of datapaths

Abstract: High-Level Synthesis deals with the translation of algorithmic descriptions into an RTL implementation. It is highly multiobjective in nature, necessitating trade-offs between mutually conflicting objectives such as area, power and delay. Thus design space exploration is integral to the High Level Synthesis process for early assessment of the impact of these trade-offs. We propose a methodology for multi-objective optimization of Area, Power and Delay during High Level Synthesis of data paths from Data Flow Graphs (DFGs). The technique performs scheduling and allocation of functional units and registers concurrently. A novel metric based technique is incorporated into the algorithm to estimate the likelihood of a schedule to yield low-power solutions. A truemulti-objective evolutionary technique, "Nondominated Sorting Genetic Algorithm II" (NSGA II) is used in this work. Results on standard DFG benchmarks indicate that the NSGA II based approach is much faster than a weighted sum GA approach. It also yields superior solutions in terms of diversity and closeness to the true Pareto front. In addition a framework for applying another evolutionary technique: Weighted Sum Particle Swarm Optimization (WSPSO) is also reported. It is observed that compared to WSGA, WSPSO shows considerable improvement in execution time with comparable solution quality.

Structural, Optical, and Electrical Properties of Cobalt-Doped CdS Quantum Dots

Abstract: In the present work, a systematic study has been carried out to understand the influence of cobalt (Co) doping on various properties of CdS nanoparticles. CdS and Co-doped CdS quantum dots have been prepared at room temperature using a chemical precipitation method without using catalysts, capping agents, or surfactants. X-ray diffraction reveals that both undoped and Co-doped CdS nanoparticles exhibit hexagonal structure without any impurity phase, and the lattice constants of CdS nanoparticles are observed to decrease slightly with increasing cobalt concentration. High-resolution transmission electron microscopy (HRTEM) shows that the particle size of CdS and 5.02% Co-doped CdS nanoparticles is in the range of 2 nm to 4 nm. The Raman spectra of Co-doped CdS nanoparticles exhibit a red-shift compared with that of bulk CdS, which may be attributed to optical phonon confinement. The optical absorption spectra of Co-doped CdS nanoparticles also exhibit a red-shift with respect to that of CdS nanoparticles. The electrical conductivity of CdS and Co-doped CdS nanoparticles is found to increase with increasing temperature and cobalt concentration.

Voltage and frequency control in power generating system using hybrid evolutionary algorithms

Abstract: A power generating system has the responsibility to ensure that adequate power is delivered to the load, both reliably and economically. The quality of power supply is affected due to continuous and random changes in load during the operation of the power system. Hence, a power system control is required to maintain a continuous balance between power generation and load demand. Load Frequency Controller and Automatic Voltage Regulator play an important role in maintaining constant frequency and voltage in order to ensure the reliability of electric power. In order to improve the performance and stability of these control loops, proportional-integral-derivative (PID) controllers are normally used. But these fixed gain controllers fail to perform under varying load conditions and hence provide poor dynamic characteristics with a large settling time, overshoot and oscillations. In order to achieve a better dynamic performance, system stability and sustainable utilization of generating systems, PID gains must...