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Wyszukujesz frazę "Balic, J." wg kryterium: Autor


Wyświetlanie 1-3 z 3
Tytuł:
Combined system for off-line optimization and adaptive cutting force control
Autorzy:
Cus, F.
Balic, J.
Powiązania:
https://bibliotekanauki.pl/articles/100078.pdf
Data publikacji:
2010
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
manufacturing processes
adaptive cutting force control
milling simulator
neural control strateg
off-line optimization
Opis:
The choice of manufacturing processes is based on cost, time and precision. A remaining drawback of modern CNC systems is that the machining parameters, such as feed-rate, cutting speed and depth of cut, are still programmed off-line. The machining parameters are usually selected before machining accordin to programmer's experience and machining handbooks. To prevent damage and to avoid machining failure the operating conditions are usually set extremely conservative. As a result, many CNC systems are inefficient and run under the operating conditions that are far from optimal . Even if the machining parameters are optimised off-line by an optimisation algorithm they cannot be adjusted during the machining process. In this paper, a neural adaptiv controller is developed and some simulations and experiments with the neural control strategy are carried out. The results demonstrate the ability of the proposed system to effectively regulate peak forces for cutting conditions commonly encountered in end milling operations.
Źródło:
Journal of Machine Engineering; 2010, 10, 2; 25-35
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary prediction of manufacturing costs in tool manufacturing
Autorzy:
Ficko, M.
Vaupotič, B.
Balič, J.
Powiązania:
https://bibliotekanauki.pl/articles/384509.pdf
Data publikacji:
2007
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
prediction of tool manufacturing costs
case-based reasoning
genetic programming
Opis:
One of the most important factors in the offer for tool manufacture is the total manufacturing cost. Although the total manufacturing costs can be rather precisely determined by the cost analysis, this approach is not well applicable in tool-making due to cost and, particularly, time demand. Therefore, the authors propose a new approach to prediction of total manufacturing costs, which is based on case based-reasoning method and imitates the human expert. The system first abstracts from CAD-models the geometrical features, and then it calculates the similarities between the source cases and target case. The most similar cases are used for preparation of prediction by genetic programming. The genetic programming method provides the model connecting the individual geometrical features with the costs searched for. Regarding to the connections between geometrical features and tool cost of source cases the formula for calculation of tool cost of target case is being made. The experimental results show that the quality of predictions made by the intelligent system is comparable to the quality assured by the experienced expert.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2007, 1, 4; 51-58
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Intelligent prediction of milling strategy using neural networks
Autorzy:
Klancnik, S.
Balic, J.
Cus, F.
Powiązania:
https://bibliotekanauki.pl/articles/971013.pdf
Data publikacji:
2010
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
SOM neural networks
CAD/CAM system
feature extraction
milling strategy
CAD segmentation
STL model
Opis:
This paper presents the prediction of milling tool-path strategy using Artificial Neural Network (ANN), by taking the predefined technological objectives into account. In the presented case, the best possible surface quality of a machined surface was taken as the primary technological aim. This paper shows how feature extraction from a 3D CAD model, and classification using a self-organizing neural network, are done. The experimental results presented in this paper suggest that the prediction of milling strategy using the self-organizing neural network (SOM) is effective.
Źródło:
Control and Cybernetics; 2010, 39, 1; 9-24
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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