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 measurement using teaching learning based optimization algorithm and comparison with particle swarm optimization algorithm International Journal of Data and Network Science 2 2018 63 70 Contents lists available at GrowingScience International Journal of Data and Network Science homepage ijds Roundness error measurement using teaching learning based optimization algorithm and com- parison with particle swarm optimization algorithm . Pratheesh Kumara P. Prasanna Kumaarb R. Kameshwaranathb a Assistant Professor Department of Production Engineering PSG College of Technology Coimbatore-641004 India b Under Graduate Student Department of Production Engineering PSG College of Technology Coimbatore-641004 India CHRONICLE ABSTRACT Article history Form deviation of machined components need to be controlled within the required tolerance val- Received May 1 2018 ues for proper assembly and to serve the intended functional requirements. Methods like minimum Received in revised format June zone circle MZC method minimum circumscribed circle MCC method maximum inscribed 16 2018 circle MIC method and least square circle LSC method are used to evaluate roundness error. Accepted August 27 2018 Available online Roundness error evaluation includes collection of co-ordinate points on the surface of the compo- August 27 2018 nent and measurement using any of the above methods. Since manual measurement of roundness Keywords error from these co-ordinate points is time consuming and less accurate use of algorithms is Roundness error highly appreciated. A detailed study of various optimization techniques showed that all evolution- Teaching Learning Based Optimi- ary and swarm intelligence-based optimization algorithms require common control parameters zation and algorithm specific parameters. Improper tuning of these parameters either increases the com- Particle Swarm Optimization putational effort or results in local optimal .
