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Optimization of machining parameters for turning operation with multiple quality characteristics using Grey relational analysis
Franko Puh, Zoran Jurković, Mladen Perinic, Miran Brezočnik, Stipo Buljan, 2016, original scientific article

Abstract: Optimization of machining processes is essential for achieving of higher productivity and high quality products in order to remain competitive. This study investigates multi-objective optimization of turning process for an optimal parametric combination to provide the minimum surface roughness (Ra) with the maximum material-removal rate (MRR) using the Grey–Based Taguchi method. Turning parameters considered are cutting speed, feed rate and depth of cut. Nine experimental runs based on Taguchi’s L9 (34) orthogonal array were performed followed by the Grey relational analysis to solve the multi- response optimization problem. Based on the Grey relational grade value, optimum levels of parameters have been identified. The significance of parameters on overall quality characteristics of the cutting process has been evaluated by the analysis of variance (ANOVA). The optimal parameter values obtained during the study have been validated by confirmation experiment.
Keywords: ANOVA, Grey relational analysis, multi-objective optimization, Taguchi method, turning
Published in DKUM: 12.07.2017; Views: 1199; Downloads: 394
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Določevanje značilnih tehnoloških in gospodarskih parametrov med postopkom odrezovanja
Uroš Župerl, Franc Čuš, 2004, original scientific article

Abstract: 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.
Keywords: odrezovanje, pogoji rezanja, struženje, optimiranje, nedeterministični postopki, machining, cutting parameters, turning, nondeterministic optimization
Published in DKUM: 10.07.2015; Views: 1117; Downloads: 50
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A Hybrid analytical-neural network approach to the determination of optimal cutting conditions
Uroš Župerl, Franc Čuš, Bogomir Muršec, Anton Ploj, 2004, original scientific article

Abstract: In the contribution, a new hybrid optimization technique for complex optimization of cutting parameters is proposed. The developed approach is based on the maximum production rate criterion and incorporates 10 technological constrains. It describes the multi-objective techniqueof optimization of cutting conditions by means of the artificial neural network (ANN) and OPTIS routine by taking into consideration the technological, economic and organization limitations. The analytical module OPTIS selects theoptimum cutting conditions from commercial databases with respect to minimum machining costs. By selection of optimum cutting conditions, it is possible to reach a favourable ratio between the low machining costs and high productivity taking into account the given limitation of the cutting process. To reach higher precision of the predicet results, a hybrid optimization algorithm is developed and presented to ensure sample, fast and efficient optimization of all important turning parameters. _
Keywords: optimization, cutting conditions, turning, analytical-neural routine, database
Published in DKUM: 01.06.2012; Views: 2351; Downloads: 89
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Turning of high quality aluminium alloys with minimum costs
Marko Reibenschuh, Franc Čuš, Uroš Župerl, 2011, original scientific article

Abstract: Turning is one of the most common metal cutting procedures used. To optimize such a cutting process, some adaptations must be made. Implementing tool wear conditioning and the use of dry cutting can significantly lower the production cost. This research focuses on the cost effectiveness of using dry cutting in comparison with the use of cutting fluids and the use of different tool geometry. A series of tests was conducted and in the end the results are given.
Keywords: aluminium, cutting fluids, production costs, turning
Published in DKUM: 01.06.2012; Views: 1567; Downloads: 112
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Prediction of surface roughness using a feed-forward neural network
Jernej Šenveter, Simon Klančnik, Jože Balič, Franc Čuš, 2010, original scientific article

Abstract: This article presents the development of a system for predicting surface roughness, using a feed-forward neural network. The primary goal was to develop a system in order to predict with complex reliability and defined accuracy. However, this system is designed in such a way that it is also possible to use it for various other workpieces. The described system uses a neural network which receives signals at the input level. The signals then travel through all hidden levels to the output level, where the responses to input signals are received. Data are used which affects the selection of surface roughness regarding the input to the neural network. Three different inputs in total are used for the neural network. Data which represents the inputs to the neural network are encoded, so that they occupy values between 0 and 1. Adequate cutting speed, feed, and depth of cut, are selected in order to achieve an adequate surface roughness of the workpiece, using the trained neural network. This contributes to the optimisation and economy of machining, which is very important during the production of an individual product and also for an individual company or organisation when transferring the final product to the contracting authority or final customer.
Keywords: machining, turning, surface roughness, neural network
Published in DKUM: 31.05.2012; Views: 1957; Downloads: 62
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Machining process optimization by colony based cooperative search technique
Uroš Župerl, Franc Čuš, 2008, original scientific article

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.
Keywords: machining, turning, optimization, cutting parameters
Published in DKUM: 31.05.2012; Views: 1820; Downloads: 42
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Intelligent programming of CNC turning operations using genetic algorithm
Jože Balič, Miha Kovačič, Boštjan Vaupotič, 2006, original scientific article

Abstract: CAD/CAM systems are nowadays tightly connected to ensure that CAD data can be used for optimal tool path determination and generation of CNC programs for machine tools. The aim of our research is the design of a computer-aided, intelligent and genetic algorithm(GA) based programming system for CNC cutting tools selection, tool sequences planning and optimisation of cutting conditions. The first step is geometrical feature recognition and classification. On the basis of recognised features the module for GA-based determination of technological data determine cutting tools, cutting parameters (according to work piece material and cutting tool material) and detailed tool sequence planning. Material, which will be removed, is split into several cuts, each consisting of a number of basic tool movements. In thenext step, GA operations such as reproduction, crossover and mutation are applied. The process of GA-based optimisation runs in cycles in which new generations of individuals are created with increased average fitness of a population. During the evaluation of calculated results (generated NC programmes) several rules and constraints like rapid and cutting tool movement, collision, clamping and minimum machining time, which represent the fitness function, were taken into account. A case study was made for the turning operation of a rotational part. The results show that the GA-based programming has a higher efficiency. The total machining time was reduced by 16%. The demand for a high skilled worker on CAD/CAM systems and CNC machine tools was also reduced.
Keywords: CNC programming, genetic algorithm, intelligent CAM, turning, tool path generation
Published in DKUM: 30.05.2012; Views: 2897; Downloads: 96
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