YMCA University of Science and Technology

About: YMCA University of Science and Technology is a education organization based out in Faridabad, India. It is known for research contribution in the topics: Web page & Web crawler. The organization has 299 authors who have published 568 publications receiving 4547 citations.

Evaluating the Variables Affecting Flexibility in FMS by Exploratory and Confirmatory Factor Analysis

Abstract: The purpose of this paper is to investigate the factors from the variables of the flexible manufacturing system (FMS) which affect flexibility in FMS. The study was performed by conducting a cross-sectional survey within manufacturing firms in India through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). By performing EFA, factor structure is identified whereas CFA verified the factor structure of a set of observed variables. CFA is carried by structural equation modeling (SEM) statistical technique. In this paper, EFA is applied to extract the factors in FMS by The Statistical Package for Social Sciences (SPSS 20) software and confirming these factors by CFA through Analysis of Moment Structures (AMOS 18) software. Fifteen variables are identified through literature, and four factors extracted, which affects the flexibility of FMS in Production Flexibility, Machine Flexibility, Product Flexibility, and Volume Flexibility. SEM using AMOS 18.0 was used to perform the first-order four-factor structure (Production Flexibility, Machine Flexibility, Product Flexibility and Volume Flexibility) of the FMS flexibility.

Deep Web Information Retrieval Process: A Technical Survey

Abstract: Web crawlers specialize in downloading web content and analyzing and indexing from surface web, consisting of interlinked HTML pages. Web crawlers have limitations if the data is behind the query interface. Response depends on the querying party's context in order to engage in dialogue and negotiate for the information. In this article, the authors discuss deep web searching techniques. A survey of technical literature on deep web searching contributes to the development of a general framework. Existing frameworks and mechanisms of present web crawlers are taxonomically classified into four steps and analyzed to find limitations in searching the deep web.

Dry sliding wear behaviour of AA6082-T6/SiC/B4C hybrid metal matrix composites using response surface methodology:

Abstract: In the present work, dry sliding wear behaviour of hybrid aluminum metal matrix composites is carried out. A mixture of silicon carbide and boron carbide is used in equal fraction as reinforcement ...

Parametric study of the dry sliding wear behaviour of AA6082-T6/SiC and AA6082-T6/B4C composites using RSM

Abstract: In this paper a parametric study of the wear behaviour of Aluminum matrix composites has been carried out. AA6082-T6/SiC and AA6082-T6/B4C composites were fabricated using stir casting technique. The percentage of reinforcement was taken as 5, 10, 15 and 20 wt.% for both SiC and B4C particulates. Dry sliding wear tests were conducted using pin-on-disc apparatus at room temperature and process optimization was done using Response surface methodology (RSM). Weight percentage (wt.%) of reinforcement, sliding speed, load and sliding distance were the four process parameters considered to analyse these composites wear behaviour. Analysis of variance (ANOVA) showed that sliding distance exerted the highest contribution (60.24 %) to AA6082-T6/SiC wear, followed by sliding speed (14.28 %), load (11.88 %) and reinforcement content (4.31 %). The same trend was found in AA6082-T6/B4C composites with slightly different contribution values, namely sliding distance (63.28 %), sliding speed (14.02 %), load (10.10 %) and reinforcement content (4.05 %). RSM analysis revealed that increases in the reinforcement content and sliding speed reduce the wear rate in both composites. On the other hand, increases in load and sliding distance led to higher AA6082-T6/SiC and AA6082-T6/B4C composites wear. The two predictive models were validated by conducting confirmation tests and certified that the developed wear predictive models are accurate and can be used as predictive tools for wear apllications.