Time filter

Source Type

As Sib al Jadidah, Oman

Vishnupriyan S.,Caledonian College of Engineering
Assembly Automation | Year: 2012

Purpose - Source errors in a workpiece fixture system include the compliance of the workpiece fixture system and workpiece dynamics. The purpose of this paper is to study the relative significance of these two. The findings would help to achieve computational economy in optimization of fixture layout and/or clamping forces. Design/methodology/approach - Different layouts are generated with the help of a reconfigurable fixture set up and a slot is end milled on the workpiece. Using these data and the finite element software ANSYS, the machining error due to system compliance is computed. The machining error due to workpiece dynamics is obtained using a data acquisition system with the LabView software. These steps are repeated for different clamping forces and the relative contribution of these two sources to the overall machining error is studied. Findings - Results show that the system compliance is much smaller in magnitude compared to workpiece dynamics and hence does not contribute appreciably to the overall machining error. This leads to the conclusion that, for bulky and stiff parts, evaluation of the machining error due to compliance can be done away with. Originality/value - The paper's originality lies in comparing the two sources of machining error using experimental work and finite element models. To the author's knowledge such a comparison has not been reported in the literature. © Emerald Group Publishing Limited.

Balamuralikrishnan R.,Caledonian College of Engineering
Asian Journal of Civil Engineering | Year: 2015

This paper explores the flexural behaviour of carbon fiber reinforced polymer (CFRP) retrofitted reinforced concrete (RC) beams. For flexural strengthening of RC beams, a total of sixteen beams were cast and tested over an effective span of 3000 mm up to failure under static monotonic and compression cyclic loads. The beams were designed as underreinforced concrete beams. Twelve beams were retrofitted with bonded CFRP fabrics in one layer, two layers and three layers which are parallel to beam axis at the bottom under virgin condition and tested until failure; the remaining four beams were used as control specimens. Static and cyclic responses of all the beams were evaluated in terms of strength, stiffness, ductility ratio, energy absorption capacity factor, compositeness between CFRP fabrics and concrete, and the associated failure modes. The theoretical moment-curvature relationship and the load-displacement response of the retrofitted beams and control beams were predicted by using FEA software ANSYS. Comparison has been made between the numerical (ANSYS) and the experimental results. The results show that the retrofitted beams exhibit increased flexural strength, enhanced flexural stiffness, and composite action until failure.

Muralidharan R.,Caledonian College of Engineering
International Journal of Ambient Energy | Year: 2016

The significance of bio-inspired evolutionary algorithms has attracted many applications for obtaining best solutions to their optimisation problems in the past decades. This paper is about the application of one of these algorithms, namely, quantum particle swarm optimisation algorithm for parameter extraction of solar photovoltaic cells using current–voltage (I–V) characteristics. This algorithm has been used here to extract five parameters, namely, photocurrent, saturation current, series resistance, shunt resistance and ideality factor that influence the I–V relationship of single diode model solar photovoltaic cells. This approach has been validated for a cell and a module. Simulations using Matlab software have shown that the simulated I–V characteristics obtained using the extracted parameters have good agreement with the experimental I–V values. The reason for the interest taken in undertaking this work is to suggest a good and an accurate simulator for solar system designers. © 2016 Taylor & Francis

Murali R.V.,Caledonian College of Engineering
Journal of Engineering and Applied Sciences | Year: 2015

In this attempt, the researcher aims to formulate an optimized worker assignment model for virtual cells by using Fuzzy Inference System (FIS) which is regarded as a successful programming technique using words rather than numbers. Improved productivity and superior quality in operations with maximum utilization of existing resources, i.e., physical and human resources are always the primary objective of manufacturing organizations. Many manufacturing philosophies have been developed to achieve the above objective and Virtual Cellular Manufacturing System (VCMS), a logical extension of Cellular Manufacturing System (CMS) is one of such philosophy developed quite recently. Worker assignment problems in the above VCMS context are highly non-linear and dynamic in nature because machineries and workforces in VCMS environment are virtually and logically rearranged to meet a particular production requirement. Tasks of workforce assignments into virtual manufacturing cells are very well handled by the researchers and various techniques namely mathematical programming models including Integer Programming (IP) and Goal Programming (GP) are developed for meeting the static and dynamic production conditions. Application of Artificial Neural Networks (ANN) into worker assignment shows enough potential as revealed from researcher's previous attempts recently. In the current attempt, the researcher extends his research efforts on worker assignment problems and presents a novel method of doing the above assignment using Fuzzy Inference System (FIS) which is regarded as a successful programming technique using words rather than numbers. Fuzzy logic, the core substance of FIS is a convenient way to map an input dataset to an output dataset and in this study datasets corresponding to two cell configuration problem under VCMS environment are used as input and output data. Results of worker assignments from the present attempt and the previous models proposed are then compared, analysed and discussed. The study and results obtained affirm that FIS also shows prominence and promise in solving problems related to workforce assignment into virtual cells. © Medwell Journals, 2015.

Bachmann R.T.,University of Kuala Lumpur | Johnson A.C.,Caledonian College of Engineering | Edyvean R.G.J.,University of Sheffield
International Biodeterioration and Biodegradation | Year: 2014

A significant quantum of crude oil is trapped in reservoirs and often unrecoverable by conventional oil recovery methods. Further downstream, the petroleum industry is facing challenges to remove sulfur, metal, nitrogen as well as undesirable organic compounds from the crude. Conventional secondary recovery methods such as water and gas injections helped to increase the productivity of the well, while chemical and physical refinery processes such as hydrodesulfurization, desalting, and high-pressure high-temperature hydrotreating remove most inorganic impurities. The increasing demand for oil in the world coupled with very stringent environmental laws piled economical and technical pressure upon the refinery industry to further improve crude oil recovery as well as reduce sulfur, metal and nitrogen concentration to the low ppm levels.In the search for economical and environmentally friendly solutions, growing attention has been given to biotechnology such as the use of microbial enhanced oil recovery (MEOR). MEOR is an alternate recovery method that uses microorganisms and their metabolic products. In addition, the emerging field of crude oil refining and associated industrial processes such as biodesulfurization, biodemetallation, biodenitrogenation and biotransformation are also covered.This review aims to provide an overview on MEOR and biorefining relevant to the petroleum industry and highlights challenges that need to be overcome to become commercially successful. Literature pertaining to laboratory experiments, field trials and patents are included in view of industrial applications and further developments. © 2013 Elsevier Ltd.

Discover hidden collaborations