Wyniki wyszukiwania - Biblioteka Nauki

1

Nowoczesne metody projektowania i wytwarzania narzędzi - na przykładzie przemysłu szklarskiego

100%

Balic J.

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2000

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tom R. 5, z. 1/2

162-175

PL

Artykuł dotyczy zwinnego wytwarzania i wprowadzenia nowych technologii do produkcji narzędzi dla potrzeb przemysłu szklarskiego przy produkcji butelek. Omawia zasady równoczesnego konstruowania i wytwarzania (agile manufacturing). Opracowano pilotażowy system elastycznej produkcji i przedstawiono analizą przypadku zaprojektowania, zaprogramowania wytwarzania i zbudowania narzędzia dla potrzeb produkcji butelek.

EN

The paper deals with the development and introducing of new technologies in production of tools for bottle and glass industry. The principles of simultaneous engineering and agile manufacturing are discussed. Pilot FMS was developed and a case study for design, manufacturing programming and manufacturing of tool for bottle industry is presented.

2

Cutting conditions from different data bases and tool flow in flexible manufacturing system

63%

Cus F. , Balic J.

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tom Vol. 4, no spec.

127-132

EN

The contribution presents the determination and selection of optimal conditions reducing the manufacturing costs. The data can be selected from different bases and are optimized with respect to boundary conditions of the own production. By taking into account the simultaneous engineering method it is necessary to carry out integrated and parallel activities of the detail design and production process. For building the tool data bank the "specialities" of the own production are taken into account. For the analysis of the tool and workpiece flow the principle of "virtual production" is considered. Accurate cost analyses are made for planning of the needs. In the processes of simultaneous optimization all requirements and the technology strategy are considered in order to meet the criteria of low costs and maximum quality. The researches and the results were tested in the real production environment.

3

Optimizing scale factors of the PolyJet™ rapid prototyping procedure by genetic programming

51%

Brajlih T. , Drstvensek I. , Kovacic M. , Balic J.

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2006

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tom Vol. 16, nr 1-2

101--106

EN

Purpose: The main purpose of our article is to represent results of our research that investigated the implementation of genetic programming methods into optimization process of the scale factor values used in PolyJet™ rapid prototyping procedures. Design/methodology/approach: The first step in our research was to test the influence of the recommended scale factor values on the dimensional accuracy of the finished parts. Then, the genetic programming was used in optimization of scale factor values regarding to the part’s properties. Finally, the optimized values were tested on another test series of parts. Findings: The optimized scale factor values yield better results in terms of accuracy than values recommended by the manufacturer. Research limitations/implications: Due to the large increase in part’s build time/cost the data range of the Z-axis dimensions of our test series was somewhat narrow, leaving the detailed study of Z-axis scale factor values for further research. Practical implications: The optimized scale factor values can be used in the RP machine software package in order to achieve higher accuracy of manufactured prototypes. Originality/value: This paper can be used as a guideline in implementation of genetic programming in optimization process of various manufacturing parameters of RP technologies. Additionally, any user of the PolyJet™ RP machine can use optimized scale factor values described in the paper.

4

Intelligent prediction of milling strategy using neural networks

51%

Klancnik S. , Balic J. , Cus F.

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tom Vol. 39, no 1

9-24

EN

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.

5

Hybrid ANFIS-ants system based optimisation of turning parameters

51%

Cus F. , Balic J. , Zuperl U.

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tom Vol. 36, nr 1

79-86

EN

Purpose: The paper presents a new hybrid multi-objective optimization technique, based on ant colony optimization algorithm (ACO), to optimize the machining parameters in turning processes. Design/methodology/approach: 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. Findings: ACO algorithm is completely generalized and problem independent so it can be easily modified to optimize this turning operation under various economic criteria. It can obtain a near-optimal solution in an extremely large solution space within a reasonable computation time. Research limitations/implications: The developed hybrid system can be also extended to other machining problems such as milling operations. The results of the proposed approach are compared with results of three non-traditional techniques (GA, SA and PSO). Among the four algorithms, ACO outperforms GA and SA algorithms. Practical implications: 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. Originality/value: New evolutionary ACO is explained in detail. Also a comprehensive user-friendly software package has been developed to obtain the optimal cutting parameters using the proposed algorithm.

6

Intelligent cutting tool condition monitoring in milling

51%

Zuperl U. , Cus F. , Balic J.

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tom Vol. 49, nr 2

477--486

EN

Purpose: of this paper is to present a tool condition monitoring (TCM) system that can detect tool breakage in real time by using a combination of neural decision system, ANFIS tool wear estimator and machining error compensation module. Design/methodology/approach: The principal presumption was that the force signals contain the most useful information for determining the tool condition. Therefore, ANFIS method is used to extract the features of tool states from cutting force signals. The trained ANFIS model of tool wear is then merged with a neural network for identifying tool wear condition (fresh, worn). Findings: The overall machining error is predicted with very high accuracy by using the deflection module and a large percentage of it is eliminated through the proposed error compensation process. Research limitations/implications: This study also briefly presents a compensation method in milling in order to take into account tool deflection during cutting condition optimization or tool-path generation. The results indicate that surface errors due to tool deflections can be reduced by 65-78%. Practical implications: The fundamental limitation of research was to develop a single-sensor monitoring system, reliable as commercially available system, but much cheaper than multi-sensor approach. Originality/value: A neural network is used in TCM as a decision making system to discriminate different malfunction states from measured signals.