Applied Mechanics and Materials Vol. 573

Abstract: Software Reuse can improve the development time, cost and quality of Software artifacts. The Storage of artifacts plays an important role of easy retrieval of the needed components according to the requirement. In this paper a great measure has been taken for the retrieval of relevant component from the Ontology based repository. Two famous evolutionary algorithms Genetic Algorithm and Particle Swarm Optimization algorithm were used for extraction of needed component. These two algorithms are separately used for component retrieval. Genetic Algorithm in Component Retrieval is best suited if the repository has more number of relevant components. PSO for Component search is best suited if the query is highly refined to get more relevant document. PSO is used for the mainly query expansion. These two methods are combined first the retrieved set of component is organized with the help of GA and PSO for best query expansion. Thus these two methods are combined for best precision and retrieval time for different sets of requirement query

Abstract: Web intelligence provides a platform that empowers internet users to determine the most appropriate and best information for their interests. It provides the ability to sense and adapt to the needs and preference of the user. The recent advancements have made it conceivable to capture the users experience and interactions with web. Consequently predicting users behaviors will expedite and enhance browsing experience. This paper proposes an intelligent approach for making the web more powerful by predicting the conduct of individual users. The main goal is to implicitly construct user profiles using a Particle Swarm Optimization - based technique. We reveal interesting results in comparing with a standard user modeling approach.

Abstract: The measurement of surface roughness of the machined Fiber Reinforced Plastics is very important to assess the quality of a composite, which is normally carried out using taly-surf stylus instruments. This method of measuring is accepted widely by all the researchers. But, this process is not suitable for high volume applications as it is time consuming and cumbersome. With rising demand of industrial automation in manufacturing process, image processing technique plays an important role in inspection and process monitoring. In this paper, a new parameter for determining surface roughness of machined fiber reinforced specimens was proposed using image processing technique. The experimental result indicates that the surface roughness of machined composites could be predicted with a reasonable accuracy using image processing technique.

Abstract: Inconel 625 materials are widely used in the chemical, aircraft and shipbuilding industries due to thermal resistant retaining mechanical properties. These materials are generally hard to machine material due to their high strength and high work hardening tendency. Surface roughness (SR) and material removal rate (MRR) are widely considered as demanding aspect causing poor quality in machining. Optimization of cutting parameter is essential for the achievement of high quality and high rate of production. In this work, TiAlN coated cutting tool was used for CNC turning of Inconel 625 under dry conditions and optimum cutting parameters for each performance measure is obtained by employing Taguchi techniques. Analysis of variance (ANOVA) was employed to study the performance characteristics in turning operation.

Abstract: AISI 1040 is a high quality, unalloyed medium carbon steel usually good ductility with resistance to wear. Tool wear (TW) and surface roughness (SR) are mostly considered as demanding phases, and thus causing poor results in machining operations. Optimization of cutting parameter is more crucial at this condition for improving the quality of the product. Taguchi method is the method to achieve a robust experimental design in the study of product quality is an important issue. The best factors/levels combination with lowest societal cost solution to achieve customers requirements look by Taguchi method. Various cutting speed, feed and depth of cut are taken as parameters. In this study single response optimization was performed in computer numerically controlled (CNC) turning AISI1040 with TiCN/TiN coated cutting tool under dry condition using Taguchi Techniques with the objective of minimization SR and TW. Analysis of variance (ANOVA) was used for identifying the significant parameters affecting the responses.

Abstract: Stainless steels are used in aerospace, automotive, marine applications, because of resistant to corrosion and maintaining their mechanical properties over a wide range of temperature. Stainless steels are generally difficult to machine due to their high strength. The machining parameters which are affecting the quality of turning operation, it is necessary to optimize the machining parameters to obtain better productivity. The aim of the study is to investigate the influence of different coated tools on austenitic stainless steel (AISI316) and martensitic stainless steel (AISI410) in CNC turning under dry conditions. Multi response optimization of machining parameters was performed using coated with TiCN/Al₂O₃, TiAlN, Ti (C, N, B) using grey relational analysis.

Abstract: Automotive components made from composite materials can result in significant weight savings over steel and Aluminum. The main purpose of this research is to study about the selection of suitable composite material for automobile torsion bar which possesses good strength to weight ratio and yield considerable weight savings. This paper involves identification of potential composite materials, selection of evaluation criteria, use of fuzzy theory to quantify criteria values under uncertainty and application of fuzzy Linguistics to evaluate and select the best material for replacing conventional steel material with composite material used in automobile torsion bar. The strength of the proposed paper is the ability to deal with uncertainty arising due to the lack of real data in material selection for replacing the conventional material. Keyw ords:- Composite material, Incomplete linguistic preference relations, AHP, Decision analysis, Consistent fuzzy preference relations, Multi-criteria decision making

Abstract: Glass fiber reinforced polymers (GFRP) have been used in variety of engineering applications, owing its corrosive resistance property. Machining of GFRP material is difficult to carry out due to the non homogenous structure of material. Optimization of parameters is essential for achieve the quality of product. In this paper, Turning parameter optimization is studied for machining GFRP under dry conditions using coated tool. Single response optimization is performed by Taguchi method.

Abstract: With the deregulation of electricity markets, the system operation strategies have changed in recent years. The systems are operated with smaller margins. How to maintain the voltage stability of the power systems have become an important issue.This paper presents an Artificial Feed Forward Neural Network (FFNN) approach for the assessment of power system voltage stability. This paper uses some input feature sets using real power, reactive power, voltage magnitude and phase angle to train the neural network (NN). The target output for each input pattern is obtained by computing the distance to voltage collapse from the current system operating point using a continuation power flow type algorithm. This paper compared different input feature sets and showed that reactive power and the phase angle are the best predictors of voltage stability margin. Further, the paper shows that the proposed ANN based method can successfully estimate the voltage stability margin not only under normal operation but also under N-1 contingency situations. The proposed method has been applied to the IEEE 14 and IEEE 30 bus test system. The continuation power flow technique run with PSAT and the proposed method is implemented in MATLAB.

Abstract: This paper deals with the mitigation of voltage sag and harmonic profile improvement in a microgrid system. The microgrid system contains a hybrid combination of PV array, Battery interfaced with a cascaded multilevel inverter through a boost converter. The microgrid feeds a non-linear balanced load. The occurrence of voltage sag in the microgrid is compensated using the reference current for mitigation by using the SRF theory. The proposed power quality conditioner can compensate the voltage variations and harmonic profile distortions caused by the load changes. The efficacy of the proposed power quality conditioner in the microgrid system is validated through the MATLAB/Simulink.