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.

Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

Characterization of Histone H2A Derived Antimicrobial Peptides, Harriottins, from Sicklefin Chimaera Neoharriotta pinnata (Schnakenbeck, 1931) and Its Evolutionary Divergence with respect to CO1 and Histone H2A

Antimicrobial peptides (AMPs) are humoral innate immune components of fishes that provide protection against pathogenic infections. Histone derived antimicrobial peptides are reported to actively participate in the immune defenses of fishes. Present study deals with identification of putative antimicrobial sequences from the histone H2A of sicklefin chimaera, Neoharriotta pinnata. A 52 amino acid residue termed Harriottin-1, a 40 amino acid Harriottin-2, and a 21 mer Harriottin-3 were identified to possess antimicrobial sequence motif. Physicochemical properties andmolecular structure ofHarriottins are in agreement with the characteristic features of antimicrobial peptides, indicating its potential role in innate immunity of sicklefin chimaera. The histone H2A sequence of sicklefin chimera was found to differ from previously reported histone H2A sequences. Phylogenetic analysis based on histone H2A and cytochrome oxidase subunit-1 (CO1) gene revealed N. pinnata to occupy an intermediate position with respect to invertebrates and vertebrates

Predicted impact of the sea-level rise at VellareColeroon estuarine region of Tamil Nadu coast in India: Mainstreaming adaptation as a coastal zone management option

Low-lying coastal areas are more vulnerable to the impacts of climate change as they are highly prone for inundation to SLR (Sea-Level Rise). This study presents an appraisal of the impacts of SLR on the coastal natural resources and its dependent social communities in the low-lying area of VellareColeroon estuarine region of the Tamil Nadu coast, India. Digital Elevation Model (DEM) derived from SRTM 90M (Shuttle Radar Topographic Mission) data, along with GIS (Geographic Information System) techniques are used to identify an area of inundation in the study site. The vulnerability of coastal areas in Vellar-Coleroon estuarine region of Tamil Nadu coast to inundation was calculated based on the projected SLR scenarios of 0.5 m and 1 m. The results demonstrated that about 1570 ha of the LULC (Land use and Land cover) of the study area would be permanently inundated to 0.5 m and 2407 ha for 1 m SLR and has also resulted in the loss of three major coastal natural resources like coastal agriculture, mangroves and aquaculture. It has been identified that six hamlets of the social communities who depend on these resources are at high-risk and vulnerable to 0.5 m SLR and 12 hamlets for 1 m SLR. From the study, it has been emphasized that mainstreaming adaptation options to SLR should be embedded within a coastal zone management and planning effort, which includes all coastal natural resources (ecosystem-based adaptation), and its dependent social communities (community-based adaptation) involved through capacity building