Zveza strojnih inženirjev in tehnikov Slovenije etc., = Association of Mechanical Engineers and Technicians of Slovenia etc.
Abstract
Research of economics of multi-pass machining operations has significant practical importance. Non-traditional optimization techniques such genetic algorithms, neural networks and PSO optimization are increasingly used to solve optimization problems. This paper presents a new multi-objective optimization technique, based on ant colony optimization algorithm (ACO), to optimize the machining parameters in turning processes. 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. New evolutionary ACO is explained in detail. Also a comprehensive userfriendly software package has been developed to obtain the optimal cutting parameters using the proposed algorithm. 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.Preučevanje ekonomike pri opravilih obdelave z večimi prehodi ima pomembno praktično pomembnost. Ne-tradicionalne optimizacijske tehnike kot so genetski algoritmi, nevronske mreže in PSO optimizacija so vsepogosteje uporabljene pri reševanju optimizacijskih problemov. V prispevku je predstavljena več-ciljna optimizacijska tehnika, ki temelji na algoritmu kolonije mravelj (ACO) in je uporabljena pri optimiranju rezalnih parametrov pri postopkih struženja. S tehniko se simultano optimirajo naslednji trije nasprotujoči si ciljni dejavniki: stroški opravila, čas obdelave in kakovost površine. Za ciljno funkcijo je uporabljena funkcija, ki maksimira dobiček opravila. Predlagan pristop uporabi prilagodni nevro-mehki inferenčni sistem (ANFIS) za predstavitev ciljne funkcije proizvajalca in algoritem kolonije mravelj (ACO) za določitev optimalnih ciljnih vrednosti. Nova razvojna tehnika ACO je podrobno predstavljena. Razvit je obsežen uporabniku prijazen programski paket za določevanje optimalnih rezalnih parametrov z uporabo predlaganega algoritma. Na primeru je prikazana uporabnost sistema in njegova učinkovitost.Rezultate smo primerjali in analizirali z metodami drugih raziskovalcev in priporočili v katalogih. Rezultati nakazujejo, da je predlagana paradigma kolonije mravelj učinkovita v primerjavi s tehnikami drugih raziskovalcev
