Roundness error measurement using teaching learning based optimization algorithm and comparison with particle swarm optimization algorithm

Kumar, M.; Kumaar, P.; Kameshwaranath, R.; Thasarathan, R.

Growing Science » International Journal of Data and Network Science » Roundness error measurement using teaching learning based optimization algorithm and comparison with particle swarm optimization algorithm

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International Journal of Data and Network Science

ISSN 2561-8156 (Online) - ISSN 2561-8148 (Print) Quarterly Publication Volume 2 Issue 3 pp. 63-70 , 2018

Roundness error measurement using teaching learning based optimization algorithm and comparison with particle swarm optimization algorithm Pages 63-70 Download PDF

Authors: M.R. Pratheesh Kumar, P. Prasanna Kumaar, R. Kameshwaranath, R. Thasarathan

DOI: 10.5267/j.ijdns.2018.8.003

Keywords: Roundness error, Teaching Learning Based Optimization, Particle Swarm Optimization Minimum zone circle, Least square circle

Abstract: Form deviation of machined components need to be controlled within the required tolerance values for proper assembly and to serve the intended functional requirements. Methods like minimum zone circle (MZC) method, minimum circumscribed circle (MCC) method, maximum inscribed circle (MIC) method and least square circle (LSC) method are used to evaluate roundness error. Roundness error evaluation includes collection of co-ordinate points on the surface of the compo-nent and measurement using any of the above methods. Since, manual measurement of roundness error from these coordinate points is time consuming and less accurate, use of algorithms is highly appreciated. A detailed study of various optimization techniques showed that all evolutionary and swarm intelligence-based optimization algorithms require common control parameters and algorithm specific parameters. Improper tuning of these parameters either increases the computational effort or results in local optimal solution. Teaching Learning Based Optimization (TLBO) algorithm is used in this work as it does not require any algorithm specific parameters for the evaluation of roundness error. The results obtained are then compared with the results of Particle Swarm Optimization (PSO) algorithm to know the merits and demerits of both the algorithms when used for form measurement.

How to cite this paper

 Kumar, M., Kumaar, P., Kameshwaranath, R & Thasarathan, R. (2018). Roundness error measurement using teaching learning based optimization algorithm and comparison with particle swarm optimization algorithm.International Journal of Data and Network Science, 2(3), 63-70.

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