The selection of milling parameters by the PSO-based neural network modeling method

Machining analysis of natural fibre reinforced composites using fuzzy logic

“…They also demonstrated that delamination factor and surface roughness depends on feed and cutting speed during the milling process of carbon fibre reinforced composites [2]. Farahnakian et al [3] made investigations on polyamide added with Nano clay and concluded that feed and speed are the major factors influencing the surface roughness. Davim et al [4] studied the machining aspects of milling on medium density fibre boards and reported that feed and speed have more influence on surface roughness.…”

Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques – a Comparative Study

“…Farahnakian et al [23] studied end-milling with PSO combined with a neural network (NN) algorithm to predict surface roughness and cutting forces. Cus and Zuperl [1] compared the PSO, GA and SA techniques to estimate the cutting force, the best results were obtained with PSO.…”

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

“…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). Considering cutting force as an objective, researchers employed particle swarm optimization (Farahnakian et al, 2011), for optimization of milling process parameters. Multiobjective optimization for milling process is attempted by researchers using posteriori approaches namely non-dominated sorting genetic algorithm (NSGA) (Wang et al, 2006) and multi-objective particle swarm optimization (MPSO) (Yang et al, 2011).…”