Optimasi Produktivitas Pemesinan Keras Baja Paduan dengan Metode Rsm-Pso

Abstract

The PSO (Particle Swarm Optimization) algorithm is one of the optimization algorithms that can be used for decision making. But it can also be used to search for optimization values under certain conditions. This is used to analyze and find the optimum value in the existing machining conditions. This study compared 2 hard machining experiments using carbide and cermet chisels. Each experiment uses the Minimum Quantity Lubricant System (MQL) in the machining process. With variable cut conditions between v = [100 120 140] m/min, f = [0.1 0.15 0.2] mm/rev, P= [4 6 8 ]bar and Q = [40 60 80] bar for cermet chisels and v = [90 120] m/min, f = [0.1 0.2] mm/rev, a= [0.25 0.5] mm and CE = [Dry MQL] . Using the Response Surface Methodology method, a regression equation is obtained for the Variable Tool Wear Response (Vb) and Surface Roughness (Ra) which will be optimized using the PSO (Particle Swarm Optimization) Algorithm in Matlab. The lowest optimum value for the Carbide Vb tool was 0.015914503 mkrions under cutting conditions v = 100 m/min, f = 0.161894308 mm/rev, P = 8 Bar and Q = 80 ml/hour and the lowest optimum Ra value was 0.2122 mkrions under cutting conditions v = 100 m/min, f = 0.1 mm/rev, P = 8 Bar and Q = 40 ml/hour and for Cermet Chisels the optimum Vb is 232 mkrions, Ra Optimum is 1,143 microns and Optimum Machining Power (P) is 314,425 Watt .