Hybrid ANFIS-ants system based optimisation of turning parameters

Purpose: The paper presents a new hybrid multi-objective optimization technique, based on ant colony optimization algorithm (ACO), to optimize the machining parameters in turning processes. Design/methodology/approach: Three conflicting objectives, production cost, operation time and cutting quality are simultaneously optimized. An objective function based on maximum profit in operation has been used. The proposed approach uses adaptive neuro-fuzzy inference system (ANFIS) system to represent the manufacturer objective function and an ant colony optimization algorithm (ACO) to obtain the optimal objective value. Findings: ACO algorithm is completely generalized and problem independent so it can be easily modified to optimize this turning operation under various economic criteria. It can obtain a near-optimal solution in an extremely large solution space within a reasonable computation time. Research limitations/implications: The developed hybrid system can be also extended to other machining problems such as milling operations. The results of the proposed approach are compared with results of three nontraditional techniques (GA, SA and PSO). Among the four algorithms, ACO outperforms GA and SA algorithms. Practical implications: An example has been presented to give a clear picture from the application of the system and its efficiency. The results are compared and analysed using methods of other researchers and handbook recommendations. The results indicate that the proposed ant colony paradigm is effective compared to other techniques carried out by other researchers. Originality/value: New evolutionary ACO is explained in detail. Also a comprehensive user-friendly software package has been developed to obtain the optimal cutting parameters using the proposed algorithm.