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2. Impact of various rpm levels on rotary and pendulum spreaders performanceBogomir Muršec, Anton Ploj, 2002, original scientific article Abstract: The paper demonstrates how different levels of shaft revolutions affect the performance of mono-disk rotary and pendulum fertiliser widely used by Slovenian farmers. Tests have been conducted on mono-disk rotary spreader manufactured by the Italian company AGREX, and on a pendulum spreader manufactured by Slovenian companz CREINA fr5om Kranj. To establish the impact of different revolution levels on the transverse spread-dispersion, spread width and flow of a fertiliser, both types of spreaders have been submitted totesting at different rpm levels of the tractor drive shaft: at 290 rpm, 410 rpm, and 540 rpm. It has been astablished that the spread width on both spreaders statisticaly significant (P>0.05) increases with the increase of the rpm`s. The fertiliyer flow statistically significant (P<0.05) changes onlyon the pendulum spreaders, whereas on rotary spreaders it has been found out that the fertilizer flow does not depend on the rpm`s.
Keywords: rotary spreader, pendulum spreader, spread width, spread flow
Published in DKUM: 10.07.2015; Views: 1751; Downloads: 38
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3. A Hybrid analytical-neural network approach to the determination of optimal cutting conditionsUroš 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: 2474; Downloads: 94
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4. A generalized neural network model of ball-end milling force systemUroš Župerl, Franc Čuš, Bogomir Muršec, Anton Ploj, 2005, original scientific article Abstract: 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%.
Keywords: end-milling, cutting forces, cutting parameters, generalized neural networks, modeling
Published in DKUM: 01.06.2012; Views: 2688; Downloads: 97
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