Manufacturing Technology Today

J. Prasanna ¹, P. Mannar Jawahar ², B. Mohan ³

Affiliations
1 Dept. of Mechanical Engineering, College of Engineering, Anna University, Chennai-600025, IN
2 Centre for University, Industry Collaboration, Anna University, Chennai-600025, IN
3 Dept. of Production Technology, Madras Institute of Technology, Anna University, Chennai-600044, IN

Abstract

Material removal in the electrical discharge machining (EDM) is primarily due to a thermal mechanism. Therefore, the analysis of plasma in EDM is a crucial one to realize the concept of the machining. This work looks into the temperature measurement of the EDM plasma by using a newly designed optical system. The measuring system consists of an optical fiber positioned in the dielectric oil and spectrometers interfaced with charge coupled devices (CCDs). Spectral data of the plasma spark was analyzed by means of image-processing technique in-turn to measure the relative intensities of Fe I spectral lines emitted during the machining. The outcome of this study reveals that the temperature of the EDM plasma varies from 7965° K to 9680° K for a significant range of gap current.

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K. M. Abubacker ¹, P. Mannar Jawahar ²

Affiliations
1 Department of Mechanical Engineering, B.S.A. Crescent Engineering College, Chennai, IN
2 Department of Automobile Engineering, MIT Campus, Anna University, Chennai, IN

Abstract

Searching the optimal solution for constrained optimization problems take more computational time in locating feasible solutions, when the percentage of feasible region within the search space is very small. Genetic algorithms prove its robustness in finding the optimal solution and necessitate more population diversity in locating the feasible region. The selective pressure has to be increased in such a way that the search does not converge to local optimal solution. Mixed sampling mechanism is opted to accomplish these conditions, which involves both stochastic and deterministic selection procedures. The algorithm presented contains these features and is attained by improving the fitness of solutions within a generation before the next generation is produced. Three test problems are solved, with the proposed ordered replacement genetic algorithms to improve the fitness of the population in every generation. Further generations are produced by proportionate selection method or tournament selection method. Both real and binary coded genetic algorithms are used to solve the test problems. An approach for optimal design of final drive in passenger bus using the proposed algorithms is presented with the objective to minimize the volume. The design search space is defined by the decision variables such as transverse module, face width, number of pinion teeth, number of gear teeth and is further influenced by the constraints such as bending stress, contact stress, face contact ratio and transmission ratio. The optimal design problem is solved using the proposed method. The results show the proficiency of ordered replacement genetic algorithms.

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