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Dhas J.E.R.,Noorul Islam Center for Higher Education | Kumanan S.,National Institute of Technology Tiruchirappalli
Materials and Manufacturing Processes | Year: 2011

Welding is one of the major manufacturing processes widely used in all industries because of its inherent properties of joining similar or dissimilar metals efficiently and economically. Residual stresses are inherent, unavoidable detrimental and have significant effect in welded structures. Researchers have developed many techniques to predict welding residual stress. Intelligent tools and techniques have been applied to predict residual stresses to meet the demands of automation in industries. Existing tools are limited in application and needs attention. This research paper addresses the development of finite element model and neurohybrid models for the prediction of residual stress in butt-welding. Residual stress model is developed by the finite element method (FEM). Data sets from FEM model are used to train the developed neuro-based hybrid models such as neural network model trained with genetic algorithm (NNGA), neural network model trained with particle swarm optimization (NNPSO), and neural network model trained with fuzzy system, adaptive neuro fuzzy inference system (ANFIS). Among the developed models, performance of ANFIS model is superior in terms of computational speed and accuracy. Developed models are validated and reported. These developed models find scope in welding shop floor environment to set the initial weld process parameters. Copyright © Taylor & Francis Group, LLC. Source


Dhas J.E.R.,Noorul Islam Center for Higher Education | Kumanan S.,National Institute of Technology Tiruchirappalli
Applied Soft Computing Journal | Year: 2011

In submerged arc welding (SAW), weld quality is greatly affected by the weld parameters such as welding current, welding speed; arc voltage and electrode stickout since they are closely related to the geometry of weld bead, a relationship which is thought to be complicated because of the non-linear characteristics. However, trial-and-error methods to determine optimal conditions incur considerable time and cost. In order to overcome these problems, non-traditional methods have been suggested. Bead-on-plate welds were carried out on mild steel plates using semi automatic SAW machine. Data were collected as per Taguchi's Design of Experiments and regression analysis was carried to establish input-output relationships of the process. By this relationship, an attempt was made to minimize weld bead width, a good indicator of bead geometry, using optimization procedures based on the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm to determine optimal weld parameters. The optimized values obtained from these techniques were compared with experimental results and presented. © 2011 Elsevier B.V. All rights reserved. Source


Dheeba J.,Noorul Islam Center for Higher Education | Albert Singh N.,Bharat Sanchar Nigam | Tamil Selvi S.,National Engineering College
Journal of Biomedical Informatics | Year: 2014

Breast cancer is the second leading cause of cancer death in women. Accurate early detection can effectively reduce the mortality rate caused by breast cancer. Masses and microcalcification clusters are an important early signs of breast cancer. However, it is often difficult to distinguish abnormalities from normal breast tissues because of their subtle appearance and ambiguous margins. Computer aided diagnosis (CAD) helps the radiologist in detecting the abnormalities in an efficient way. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network (PSOWNN). The proposed abnormality detection algorithm is based on extracting Laws Texture Energy Measures from the mammograms and classifying the suspicious regions by applying a pattern classifier. The method is applied to real clinical database of 216 mammograms collected from mammogram screening centers. The detection performance of the CAD system is analyzed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.96853 with a sensitivity 94.167% of and specificity of 92.105%. © 2014. Source


Ahilan C.,Oxford Engineering College | Kumanan S.,National Institute of Technology Tiruchirappalli | Sivakumaran N.,National Institute of Technology Tiruchirappalli | Edwin Raja Dhas J.,Noorul Islam Center for Higher Education
Applied Soft Computing Journal | Year: 2013

Decision-making process in manufacturing environment is increasingly difficult due to the rapid changes in design and demand of quality products. To make decision making process (selection of machining parameters) online, effective and efficient artificial intelligent tools like neural networks are being attempted. This paper proposes the development of neural network models for prediction of machining parameters in CNC turning process. Experiments are designed based on Taguchi's Design of Experiments (DoE) and conducted with cutting speed, feed rate, depth of cut and nose radius as the process parameters and surface roughness and power consumption as objectives. Results from experiments are used to train the developed neuro based hybrid models. Among the developed models, performance of neural network model trained with particle swarm optimization model is superior in terms of computational speed and accuracy. Developed models are validated and reported. Signal-to-noise (S/N) ratios of responses are calculated to identify the influences of process parameters using analysis of variance (ANOVA) analysis. The developed model can be used in automotive industries for deciding the machining parameters to attain quality with minimum power consumption and hence maximum productivity. © 2012 Elsevier B.V. All rights reserved. Source


Joseph J.,Noorul Islam Center for Higher Education | Boomadevi Janaki G.,Noorul Islam Center for Higher Education
Journal of Molecular Structure | Year: 2014

Novel copper complexes of 2-aminobenzothiazole derivatives were synthesized by the condensation of Knoevenagel condensate acetoacetanilide (obtained from substituted benzaldehydes and acetoacetanilide) and 2-aminobenzothiazole. They were thoroughly characterized by elemental analysis, IR, 1H NMR, UV-Vis., MS Spectra, molar conductance, magnetic moment and electrochemical studies. These spectral studies suggested that distorted square planar geometry for all the complexes. Molar conductance data and magnetic susceptibility measurements provide evidence for monomeric and neutral nature of the complexes. The electrochemical behaviour of the ligand and complexes in DMSO at 298 K was studied. The present ligand systems stabilize the unusual oxidation states of copper ion during electrolysis. Antibacterial screening of the ligands and their complexes reveal that all the complexes show higher activities than the free ligands. © 2014 Elsevier B.V. All rights reserved. Source

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