Optimal cutting condition determination for desired surface roughness in end milling

Multi Objective Optimization of End Milling Process Parameter Using Ai Techniques:a Review

“…Prakasvudhisarn et al [13] proposed an approach to determine optimal cutting condition for desired surface roughness in end milling. The approach consists of two parts: machine learning technique called support vector machine to predict surface roughness and particle swarm optimization technique for parameters optimization.…”

Process parameter optimization based on principal components analysis during machining of hardened steel

“…It is revealed from the literature that researchers had attempted single objective optimization of milling process parameters to achieve desired effects like surface roughness, production rate, machining time, production cost, tool life, and cutting force. Various methods attempted by the researchers for minimization of surface roughness include genetic algorithm (Brezocnik & Kovacic, 2003;Corso et al, 2012), simulated annealing (Corso et al, 2012), and particle swarm optimization (Prakasvidhisarn et al, 2009). The attempts are also made by researchers to maximize the production rate (or minimize machining time) using genetic algorithm (Aggarwal & Xirouchakis, 2012), simulated annealing (Rao & Pawar, 2010), artificial bee colony (Rao & Pawar, 2010), particle swarm optimization (Rao & Pawar, 2010;Gao et al, 2012), harmony search algorithm (Zarei et al, 2009), cuckoo search algorithm (Yildiz, 2012), and teaching learning based optimization algorithm (Pawar & Rao, 2013).…”

Optimization of Milling Conditions by Using Particle Swarm Optimization Technique: A Review

“…In the research by Prakasvudhisarn et al [12], process parameters of CNC end milling were selected such as feed rate, spindle speed, and depth of cut to find the minimum surface roughness. Support vector machine (SVM) was proposed to capture characteristics of roughness and its factors.…”