AbstrAct Purpose: The main objective of this paper is to present the development of an empirical model-based control mechanism to maintain a fine surface finish quality by maintaining on-line cutting values. Design/methodology/approach: The proposed model has been developed to present the control model constraints, by varying the machining parameters to control the force output to be constant. Genetic programming method (GP) has been applied to derive empirical relationship of the surface finish and the cutting force. These relationships have been applied to develop the proposed simulation model, in which the cutting force is adjusted to improve the required surface finish for the end milling operation process. Findings: The experimental results show that not only does the milling system with the design controller have high robustness, and global stability but also the machining efficiency of the milling system with the adaptive controller is much higher than for traditional CNC milling system. Experiments have confirmed efficiency of the adaptive control system, which is reflected in improved surface quality and decreased tool wear. Research limitations/implications: The proposed architecture for determining of optimal cutting conditions is applied to ball-end milling in this paper, but it is obvious that the system can be extended to other machines to improve cutting efficiency. Practical implications: The results of experiments demonstrate the ability of the proposed system to effectively regulate peak cutting forces for cutting conditions commonly encountered in end milling operations. The high accuracy of results within a wide range of machining parameters indicates that the system can be practically applied in industry. Originality/value: By the GP modeling the system for adaptive adjustment of cutting parameters is built.

AbstrAct Purpose: In this paper, Particle Swarm Optimization (PSO), which is a recently developed evolutionary technique, is used to efficiently optimize machining parameters simultaneously in high-speed milling processes where multiple conflicting objectives are present. Design/methodology/approach: Selection of machining parameters is an important step in process planning therefore a new methodology based on PSO is developed to optimize machining conditions. Artificial neural network simulation model (ANN) for milling operation is established with respect to maximum production rate, subject to a set of practical machining constraints. An ANN predictive model is used to predict cutting forces during machining and PSO algorithm is used to obtain optimum cutting speed and feed rate. Findings: The simulation results show that compared with genetic algorithms (GA) and simulated annealing (SA), the proposed algorithm can improve the quality of the solution while speeding up the convergence process. PSO is proved to be an efficient optimization algorithm. Research limitations/implications: Machining time reductions of up to 30% are observed. In addition, the new technique is found to be efficient and robust. Practical implications: The results showed that integrated system of neural networks and swarm intelligence is an effective method for solving multi-objective optimization problems. The high accuracy of results within a wide range of machining parameters indicates that the system can be practically applied in industry. Originality/value: An algorithm for PSO is developed and used to robustly and efficiently find the optimum machining conditions in end-milling. The new computational technique has several advantages and benefits and is suitable for use combined with ANN based models where no explicit relation between inputs and outputs is available. This research opens the door for a new class of optimization techniques which are based on Evolution Computation in the area of machining.