Multi-pass turning process parameter optimization using teachinglearning-based optimization algorithm

The primary objective in multi-pass turning operations is to produce products with low cost andhigh quality, with a lower number of cuts. parameter optimization plays an important role in achievingthis goal. process parameter optimization in a multi-pass turning operation usually involves the optimalselection of cutting speed, feed rate, depth of cut and number of passes. in this work, the parameter opti-mization of a multi-pass turning operation is carried out using a recently developed advanced optimiza-tion algorithm, named, the teachinglearning-based optimization algorithm. two different examples areconsidered that have been attempted previously by various researchers using different optimization tech-niques, such as simulated annealing, the genetic algorithm, the ant colony algorithm, and particle swarmoptimization, etc. the first example is a multi-objective problem and the second example is a single objec-tive multi-constrained problem with 20 constraints. the teachinglearning-based optimization algorithmhas proved its effectiveness over other algorithms.