Uroš Župerl (Author), Franc Čuš (Author), Valentina Gečevska (Author)

Abstract

Izbira rezalnih parametrov je najpomembnejši korak pri postopku načrtovanja proizvodnje, zato izdelamo novo tehniko razvojnega računanja za optimiranje procesa odrezovanja. V prispevku je uporabljena tehnika, ki oponaša dinamiko delcev v velikih skupinah (optimizacija PSO), za učinkovito in simultano optimiranje postopkov frezanja. V omenjenih postopkih smo soočeni s problemom več ciljnih dejavnikov. Najprej uporabimo umetno nevronsko mrežo (UNM) za napovedovanje rezalnih sil, nato z algoritmom PSO pridobimo optimalno rezalno hitrost in podajanja. Cilj optimizacije je, ob upoštevanju omejitev, določiti ekstrem ciljne funkcije (napovedna površina največjih sil). Med optimizacijo delci, s svojo inteligenco, letijo po prostoru rešitev in iščejo optimalne rezalne pogoje po strategiji algoritma PSO. Rezultati pokažejo, da je integriran sistem nevronskih mrež in kolektivne inteligence učinkovita metoda pri reševanju večciljnih optimizacijskih problemov. Njena velika učinkovitost na širokem območju rezalnih parametrov potrjuje, da sistem lahko praktično uporabimo v proizvodnji. Rezultati simulacij nakazujejo, da predlagan algoritem v primerjavi z rodovnimi algoritmi (GA) in simulacijskim (SA) ohlajanjem lahko poveča natančnost rešitve in konvergenco postopka. Nova tehnika razvojnega računanja ima nekoliko prednosti ter koristi in je primerna za uporabo v kombinaciji z modeli na osnovi umetnih nevronskih vezij, pri katerih niso na voljo izrecne relacije med vhodi in izhodi. Raziskava odpre vrata na področju obdelave z odrezovanjem za nov razred optimizacijskih tehnik, ki slonijo na razvojnem računanju.

Keywords

odrezovanje;končno frezanje;rezalni parametri;nevronske mreže;razvojne tehnike;optimizacija jate delcev;cutting;end-milling;cutting parameters;neural networks;evolution techniques;particle swarm optimization;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UM FS - Faculty of Mechanical Engineering
Publisher: Association of Mechanical Engineers and Technicians of Slovenia et al.
UDC: 621.941.01:004.89
COBISS: 11573526 Link will open in a new window
ISSN: 0039-2480
Parent publication: Strojniški vestnik
Views: 1063
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Other data

Secondary language: Slovenian
Secondary title: Optimization of the characteristic parameters in milling using the PSO evolution technique
Secondary abstract: The selection of machining parameters is an important step in process planning; therefore, a new evolutionary computation technique has been developed to optimize the machining process. In this paper, Particle Swarm Optimization (PSO) is used to efficiently optimize the machining parameters simultaneously in milling processes where multiple conflicting objectives are present. First, an artificial neural network (ANN) predictive model is used topredict the cutting forces during machining and then the PSO algorithm is used to obtain the optimum cutting speeds and feed rates. The goal of the optimization is to determine the objective function maximum (the predicted cutting-force surface) by considering the cutting constraints. During optimization the particles 'fly' intelligently in the solution space and search for optimal cutting conditions according to the strategies of the PSO algorithm. The results showed that an integrated system of neural networks and swarm intelligence is an effective method for solving multi-objective optimization problems. The high accuracy of the results within a wide range of machining parameters indicates that the system can be practically applied in industry. The simulation results show that compared with genetic algorithms (GAs) and simulated annealing (SA) the proposed algorithm can improve the quality of the solution while speeding up the convergence process. The new computational technique has several advantages and benefits and is suitable for use when combined with ANN-based models where no explicit relation between the inputs and the outputs is available. This research opens the door for a new class of optimization techniques that are based on evolution computation in the area of machining.
URN: URN:NBN:SI
Type (COBISS): Not categorized
Pages: str. 354-368
Volume: ǂLetn. ǂ53
Issue: ǂšt. ǂ6
Chronology: jun. 2007
ID: 1737732