Parameter optimization of modern machining processes using teachingalearning-based optimization algorithm

• Published in: Engineering applications of artificial intelligence Vol. 26; no. 1; pp. 524 - 531
• Main Authors: Venkata Rao, R, Kalyankar, V D
• Format: Journal Article
• Language: English
• Published: 01.01.2013
• Subjects: Algorithms; Expert systems; Genetic algorithms; Machining; Optimization; Process parameters; Simulated annealing; Teachers
• ISSN: 0952-1976

Modern machining processes are now-a-days widely used by manufacturing industries in order to produce high quality precise and very complex products. These modern machining processes involve large number of input parameters which may affect the cost and quality of the products. Selection of optimum machining parameters in such processes is very important to satisfy all the conflicting objectives of the process. In this research work, a newly developed advanced algorithm named ateachingalearning-based optimization (TLBO) algorithma is applied for the process parameter optimization of selected modern machining processes. This algorithm is inspired by the teachingalearning process and it works on the effect of influence of a teacher on the output of learners in a class. The important modern machining processes identified for the process parameters optimization in this work are ultrasonic machining (USM), abrasive jet machining (AJM), and wire electrical discharge machining (WEDM) process. The examples considered for these processes were attempted previously by various researchers using different optimization techniques such as genetic algorithm (GA), simulated annealing (SA), artificial bee colony algorithm (ABC), particle swarm optimization (PSO), harmony search (HS), shuffled frog leaping (SFL) etc. However, comparison between the results obtained by the proposed algorithm and those obtained by different optimization algorithms shows the better performance of the proposed algorithm.