Optimizing The Machining Process of IS 2062 E250 Steel Plates with The Boring Operation Using a Hybrid Taguchi-Pareto Box Behnken-teaching Learning-based Algorithm

In this article, a new method termed the Taguchi-Pareto-Box Behnken design teaching learning-based optimization (TPBBD–TLBO) was developed to optimize boring process, which promotes surface roughness as output. At same time, speed, feed, and depth of cut are taken inputs. The case examines experimental data from literature on IS 2062 E250 steel plates. proposed draws recent idea that argues for possible relationship between Taguchi-Pareto Box method. This used basis further argument has role in established TPBBD optimal solutions were investigated when objective function generated using case. It replaced with regression other case, python programming codes execute computations. Then concerning parameters feed rate, cut, nose radius evaluated. With TLBO method, convergence reached at 50 iterations class population 5. parametric 800 rpm 0.06 min/min 1 min 0 radius. On use function, while deployed, experienced after 200 students. solution is 1135rpm 1024 0.61 showed higher values, indicating more energy resources accomplish goals method-based function. Therefore, constitutes promising route results operation improvement.