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1.
Comparison of different optimization and process control procedures
Marko Reibenschuh, Franc Čuš, Uroš Župerl, 2010, pregledni znanstveni članek

Opis: This paper includes a comparison of different optimization methods, used for optimizing the cutting conditions during milling. It includes also a part of using soft computer techniques in process control procedures. Milling is a cutting procedure dependent of a number of variables. These variables are dependent from each other in consequence, if we change one variable, the others change too. PSO and GA algorithm are applied to the CNC milling program to improve cutting conditions, improve end finishing, reduce tool wear and reduce the stress on the tool, the machine and the machined part. At the end a summary will be given of pasted and future researches.
Ključne besede: optimization, milling, cutting parameters, LENS
Objavljeno v DKUM: 04.08.2017; Ogledov: 1097; Prenosov: 356
.pdf Celotno besedilo (481,24 KB)
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2.
Določevanje značilnih tehnoloških in gospodarskih parametrov med postopkom odrezovanja
Uroš Župerl, Franc Čuš, 2004, izvirni znanstveni članek

Opis: V prispevku je predlagan nov ne-deterministični optimizacijski postopek za kompleksno optimizacijo rezalnih parametrov pri odrezavanju. Ta postopek uporablja umetne nevronske mreže (ANN) za reševanje problema optimiranja rezalnih pogojev. Predlagan pristop temelji na kriteriju maksimalne stopnje proizvodnje in vključuje štiri tehnološke omejitve. Z izbiro optimalnih rezalnih parametrov je možno doseči ugodno razmerje med nizkimi obdelovalnimi stroški in visoko produktivnostjo ob upoštevanju podanih omejitev procesa rezanja. Eksperimentalni rezultati kažejo, da je predlagani algoritem pri reševanju nelinearnih optimizacijskih problemov s postavljenimi omejitvami učinkovit in ga je možno vključiti v inteligentne obdelovalne sisteme. Najprej je formuliran problem določitve optimalnih parametrov odrezavanja, kot več-ciljni optimizacijski problem. Nato so predlagane nevronske mreže za predstavitev proizvajalčevih prioritetnih struktur. Za demonstracijo zmogljivosti predlaganega pristopa je detajlno obravnavan nazoren primer.
Ključne besede: odrezovanje, pogoji rezanja, struženje, optimiranje, nedeterministični postopki, machining, cutting parameters, turning, nondeterministic optimization
Objavljeno v DKUM: 10.07.2015; Ogledov: 1191; Prenosov: 51
URL Povezava na celotno besedilo

3.
Optimiranje značilnih parametrov frezanja z uporabo razvojne tehnike optimizacije jate delcev
Uroš Župerl, Franc Čuš, Valentina Gečevska, 2007, izvirni znanstveni članek

Opis: 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.
Ključne besede: odrezovanje, končno frezanje, rezalni parametri, nevronske mreže, razvojne tehnike, optimizacija jate delcev, cutting, end-milling, cutting parameters, neural networks, evolution techniques, particle swarm optimization
Objavljeno v DKUM: 10.07.2015; Ogledov: 1542; Prenosov: 40
URL Povezava na celotno besedilo

4.
A generalized neural network model of ball-end milling force system
Uroš Župerl, Franc Čuš, Bogomir Muršec, Anton Ploj, 2005, izvirni znanstveni članek

Opis: The focus of this paper is to develop a reliable method to predict 3D cutting forces during ball-end milling process. This paper uses the artificial neural networks (ANNs) approach to evolve an generalized model for prediction of cutting forces, based on a set of input cutting conditions. A set of ten input milling parameters that have a major impact on the cutting forces was chosen to represent the machining conditions. The training of the networks is performed with experimental machining data. This approach greatly reduces the time-consuming mathematical work normally required for obtaining the cutting force expressions. The estimation performance of the network is evaluated through a detailed simulation study. The accuracy of an analytical model, which is a feasible alternative to the network, is compared to that of the network. With similar system parameter estimates for both methods, the network is found to be considerably more accurate than the analytical model. The results of model validation experiments on machining Ck45 are also reported. Experimental results demonstrate that this method can accurately estimate feed cutting force within an error of 4%. The results also indicate that when the combination of sigmoidal and gaussian transfer function were applied, the prediction accuracy of neural network is as high as 98%.
Ključne besede: end-milling, cutting forces, cutting parameters, generalized neural networks, modeling
Objavljeno v DKUM: 01.06.2012; Ogledov: 2623; Prenosov: 94
URL Povezava na celotno besedilo

5.
Genetic equation for the cutting force in ball-end milling
Matjaž Milfelner, Janez Kopač, Franc Čuš, Uroš Župerl, 2005, izvirni znanstveni članek

Opis: The paper presents the development of the genetic equation for the cutting force for ball-end milling process. The development of the equation combines different methods and technologies like evolutionary methods, manufacturing technology, measuring and control technology and intelligent process technology with the adequate hardware and software support. Ball-end milling is a very common machining process in modern manufacturing processes. The cutting forces play the important role for the selection of the optimal cutting parameters in ball-end milling. In many cases the cutting forces in ball-end milling are calculated by equation from the analytical cutting force model. In the paper the genetic equation for the cutting forces in ball-end milling is developed with the use of the measured cutting forces and genetic programming. The experiments were made with the system for the cutting force monitoring in ball-end milling process. The obtained results show that the developed genetic equation fits very well with the experimental data. The developed genetic equation can be used for the cutting force estimation and optimization of cutting parameters. The integration of the proposed method will lead to the reduction in production costs and production time, flexibility in machining parameter selection, and improvement of product quality.
Ključne besede: milling, ball-end mill, optimization, cutting forces, cutting parameters, genetic algorithms
Objavljeno v DKUM: 01.06.2012; Ogledov: 2269; Prenosov: 113
URL Povezava na celotno besedilo

6.
Machining process optimization by colony based cooperative search technique
Uroš Župerl, Franc Čuš, 2008, izvirni znanstveni članek

Opis: 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.
Ključne besede: machining, turning, optimization, cutting parameters
Objavljeno v DKUM: 31.05.2012; Ogledov: 1899; Prenosov: 43
URL Povezava na celotno besedilo

7.
Approach to optimization of cutting conditions by using artificial neural networks
Franc Čuš, Uroš Župerl, 2006, izvirni znanstveni članek

Opis: Optimum selection of cutting conditions importantly contribute to the increase of productivity and the reduction of costs, therefore utmost attention is paid to this problem in this contribution. In this paper, a neural network-based approach to complex optimization of cutting parameters is proposed. It describes the multi-objective technique of optimization of cutting conditions by means of the neural networks taking into consideration the technological, economic and organizational limitations. To reach higher precision of the predicted results, a neural optimization algorithm is developed and presented to ensure simple, fast and efficient optimization of all important turning parameters. The approach is suitable for fast determination of optimum cutting parameters during machining, where there is not enough time for deep analysis. To demonstrate the procedure and performance of the neural network approach, an illustrative example is discussed in detail.
Ključne besede: optimization, cutting parameter optimization, genetic algorithm, cutting parameters, neural network algorithm, machining, metal cutting
Objavljeno v DKUM: 30.05.2012; Ogledov: 2577; Prenosov: 121
URL Povezava na celotno besedilo

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