Analysis and Optimization of Machining Process Using Evolutionary Algorithms

To ensure quality of machined products at minimum machining costs and maximum machining effectiveness, it is very important to select optimum parameters when metal cutting machine tools are employed. Traditionally, the experience of the operator plays a major role in the selection of optimum metal cutting conditions. However, attaining optimum values each time by even a skilled operator is difficult. The non-linear nature of the machining process has compelled engineers to search for more effective methods to attain optimization. The design objective preceding most engineering design activities is simply to minimize the cost of production or to maximize the production efficiency. The main aim of research work reported here is to build robust optimization algorithms by exploiting ideas that nature has to offer from its backyard and using it to solve real world optimization problems in manufacturing processes.In this thesis, after conducting an exhaustive literature review, several optimization techniques used in various manufacturing processes have been identified. The selection of optimal cutting parameters, like depth of cut, feed and speed is a very important issue for every machining process. Experiments have been designed using Taguchi technique and dry turning of SS420 has been performed on Kirlosker turn master 35 lathe. Analysis using S/N and ANOVA were performed to find the optimum level and percentage of contribution of each parameter. By using S/N analysis the optimum machining parameters from the experimentation is obtained.Optimization algorithms begin with one or more design solutions supplied by the user and then iteratively check new design solutions, relative search spaces in order to achieve the true optimum solution. A mathematical model has been developed using response surface analysis for surface roughness and the model was validated using published results from literature.Methodologies in optimization such as Simulated annealing (SA), Particle Swarm Optimization (PSO), Conventional Genetic Algorithm (CGA) and Improved Genetic Algorithm (IGA) are applied to optimize machining parameters while dry turning of SS420 material. All the above algorithms were tested for their efficiency, robustness and accuracy and observe how they often outperform conventional optimization method applied to difficult real world problems. The SA, PSO, CGA and IGA codes were developed using MATLAB. For each evolutionary algorithmic method, optimum cutting conditions are provided to achieve better surface finish.The computational results using SA clearly demonstrated that the proposed solution procedure is quite capable in solving such complicated problems effectively and efficiently. Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. From the results it has been observed that PSO provides better results and also more computationally efficient.Based on the results obtained using CGA and IGA for the optimization of machining process, the proposed IGA provides better results than the conventional GA. The improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. Finally, a comparison among these algorithms were made for the specific example of dry turning of SS 420 material and arriving at optimum machining parameters of feed, cutting speed, depth of cut and tool nose radius for minimum surface roughness as the criterion. To summarize, the research work fills in conspicuous gaps between research prototypes and industry requirements, by simulating evolutionary procedures seen in nature that optimize its own systems.

Diversification In Employment Structure And Status Of Rural Women Workers In Ernakulam District

The basic objective of the present study has been to observe the process and pattern of employment diversification among the rural women workers in Ernakulam district. The evidences are that the women workers in the rural areas of the state are being increasingly diversified into the tertiary sector. The clear cut evidence for the fact that in Kerala non-agricultural employment of rural women is increasing with more and more of them getting diversified into the tertiary sector. The women get more self esteem and recognition in terms of the work being done by them. In the urban areas of the state as a poverty eradicating measure the Kerala government has already introduced a new scheme under the banner of Kudumbasree. Another fact noticed in the study that the sectoral shift of women workers has posed a grave problem to the agricultural sector. The reluctance of workers to do manual jobs on land and the prevalence of high wages among the agricultural labours has left many a cultivable area fallow or has induced farmers to shift to less labour –intensive crops. The situation is expected to worsen in future as even the high wages fail to attract the young generation to this sector. To conclude the study has fulfilled all its objectives, viz; highlighting the rural employment structure in Kerala, examining the process, pattern, determinants and consequences of diversification among rural women workers in the sample villages. Being the first of its kind at the micro level in the state it contributes to the available literature in the area enriching the database that is crucially lacking for devising projects at the village and block-level. There exists ample scope for future research of similar nature in an urban background where the secondary data-sources are hinding towards a reversal of trends from non-agriculture to agriculture.

Muscarinic M1 and M3 Receptors,IP3 and cGMP Functional Regulation in Streptozotocin Induced Diabetic Rats: Insulin and Somatotropin Induced Rejuvenation as a Function of Age

The present study describes that acetylcholine through muscarinic Ml and M3 receptors play an important role in the brain function during diabetes as a function of age. Cholinergic activity as indicated by acetylcholine esterase, a marker for cholinergic function, decreased in the brain regions - the cerebral cortex, brainstem and corpus striatum of old rats compared to young rats. in diabetic condition, it was increased in both young and old rats in cerebral cortex, and corpus striatum while in brainstem it was decreased. The functional changes in the muscarinic receptors were studied in the brain regions and it showed that muscarinic M I receptors of old rats were down regulated in cerebral cortex while in corpus striatum and brainstem it was up regulated. Muscarinic M3 receptors of old rats showed no significant change in cerebral cortex while in corpus striatum and brainstem muscarinic receptors were down regulated. During diabetes, muscarinic M I receptors were down regulated in cerebral cortex and brainstem of young rats while in corpus striatum they were up regulated. In old rats, M I receptors were up regulated in cerebral cortex, corpus striatum and in brainstem they were down regulated. Muscarinic M3 receptors were up regulated in cerebral cortex and brainstem of young rats while in corpus striatum they were down regulated. In old rats, muscarinic M l receptors were up regulated in cerebral cortex, corpus striatum and brainstem. In insulin treated diabetic rats the activity of the receptors were reversed to near control. Pancreatic muscarinic M3 receptor activity increased in the pancreas of both young and old rats during diabetes. In vitro studies using carbachol and antagonists for muscarinic Ml and M3 receptor subtypes confirmed the specific receptor mediated neurotransmitter changes during diabetes. Calcium imaging studies revealed muscarinic M I mediated Ca2 + release from the pancreatic islet cells of young and old rats. Electrophysiological studies using EEG recording in young and old rats showed a brain activity difference during diabetes. Long term low dose STH and INS treated rat brain tissues were used for gene expression of muscarinic Ml, M3, glutamate NMDARl, mGlu-5,alpha2A, beta2, GABAAa1 and GABAB, DAD2 and 5-HT 2C receptors to observe the neurotransmitter receptor functional interrelationship for integrating memory, cognition and rejuvenating brain functions in young and old. Studies on neurotransmitter receptor interaction pathways and gene expression regulation by second messengers like IP3 and cGMP in turn will lead to the development of therapeutic agents to manage diabetes and brain activity.From this study it is suggested that functional improvement of muscarinic Ml, M3, glutamate NMDAR1, mGlu-5, alpha2A, beta2, GABAAa1 and GABAB, DAD2 and 5-HT 2C receptors mediated through IP3 and cGMP will lead to therapeutic applications in the management of diabetes. Also, our results from long term low dose STH and INS treatment showed rejuvenation of the brain function which has clinical significance in maintaining healthy period of life as a function of age.

Creation and Annihilation of Fluxons in ac‐driven Semi-annular Josephson Junction

A new geometry (semiannular) for Josephson junction has been proposed and theoretical studies have shown that the new geometry is useful for electronic applications [1, 2]. In this work we study the voltage‐current response of the junction with a periodic modulation. The fluxon experiences an oscillating potential in the presence of the ac‐bias which increases the depinning current value. We show that in a system with periodic boundary conditions, average progressive motion of fluxon commences after the amplitude of the ac drive exceeds a certain threshold value. The analytic studies are justified by simulating the equation using finite‐difference method. We observe creation and annihilation of fluxons in semiannular Josephson junction with an ac‐bias in the presence of an external magnetic field.

Modelling and Analysis of Recurrent Event Data with Multiple Causes

This thesis Entitled “modelling and analysis of recurrent event data with multiple causes.Survival data is a term used for describing data that measures the time to occurrence of an event.In survival studies, the time to occurrence of an event is generally referred to as lifetime.Recurrent event data are commonly encountered in longitudinal studies when individuals are followed to observe the repeated occurrences of certain events. In many practical situations, individuals under study are exposed to the failure due to more than one causes and the eventual failure can be attributed to exactly one of these causes.The proposed model was useful in real life situations to study the effect of covariates on recurrences of certain events due to different causes.In Chapter 3, an additive hazards model for gap time distributions of recurrent event data with multiple causes was introduced. The parameter estimation and asymptotic properties were discussed .In Chapter 4, a shared frailty model for the analysis of bivariate competing risks data was presented and the estimation procedures for shared gamma frailty model, without covariates and with covariates, using EM algorithm were discussed. In Chapter 6, two nonparametric estimators for bivariate survivor function of paired recurrent event data were developed. The asymptotic properties of the estimators were studied. The proposed estimators were applied to a real life data set. Simulation studies were carried out to find the efficiency of the proposed estimators.