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Search Results: 1 - 10 of 297512 matches for " J. Balic "
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Model of automated computer aided NC machine tools programming
J. Balic
Journal of Achievements in Materials and Manufacturing Engineering , 2006,
Abstract: Purpose: Modern companies tend towards the greatest possible automation in all areas. The new control concepts of manufacturing processes required development of adequate tools for the introduction of automated control in a certain area. The paper presents such system for automated programming of CNC machine tools.Design/methodology/approach: The system is based on the previously incorporated know-how and the rules of it implementation in tool – shop. The existing manufacturing knowledge of industry tool production was collected and analysing. On this bases flow chart of all activities were made. Theoretical contribution is made in systemization of technological knowledge, which is now accessible for all workers in NC preparation units.Findings: Utilization of technology knowledge. On the basis of the recognized properties it has worked out the algorithms with which the process of manufacture, the tool and the optimum parameters selected are indirectly determined, whereas the target function was working out of the NC programme. We can first out that with information approaching of the CAM and CAPP the barriers between them, strict so far, disappear.Research limitations/implications: Till now, the system is limited to milling, drilling and similar operation. It could be extended to other machining operations (turning, grinding, wire cutting, etc.) with the same procedure. In advanced, some methods of artificial intelligence could be use.Practical implications: It is suitable for industry tools, dies and moulds production, while the system was proved in the real tool shop (production of tools for casting). The system reduces the preparation time of NC programs and could be used with any commercial available CAD/CAM/NC programming systems. Human errors are avoid or at lover level. It is important for engineers in CAD/CAM field and in tool – shops.Originality/value: The developed system is original and was not found in the literature or in the praxis. Developed method for preparation of NC programs is new and incorporate higher level of automation.
Intelligent Computer Numerical Control unit for machine tools
J. Balic
Computer Science , 2004,
Abstract: The paper describes a new CNC control unit for machining centres with learning ability and automatic intelligent generating of NC programs on the bases of a neural network, which is built-in into a CNC unit as special device. The device performs intelligent and completely automatically the NC part programs only on the bases of 2D, 2,5D or 3D computer model of prismatic part. Intervention of the operator is not needed. The neural network for milling, drilling, reaming, threading and operations alike has learned to generate NC programs in the learning module, which is a part of intelligent CAD/CAM system.
Intelligent modelling in manufacturing
J. Balic,F. Cus
Journal of Achievements in Materials and Manufacturing Engineering , 2007,
Abstract: Purpose: Modeling of production systems is very important and makes optimization of complicated relation in production system possible. The purpose of this paper is introducing artificial techniques, like Genetic Algorithms in modeling and optimization of job shop scheduling in production environment and in programming of CNC machine tools.Design/methodology/approach: Conventional methods are not suitable for solving such complicated problems. Therefore Artificial Intelligent method was used. We apply Genetic Algorithm method. Genetic Algorithms are computation methods owing their power in particular to autonomous mechanisms in biological evolution, such as selection, “survival of the fittest” (competition), and recombination.Findings: In example solutions are developed for an optimization problem of job shop scheduling by natural selection. Thus no explicit knowledge was required about how to create a good solution: the evolutionary algorithm itself implicitly builds up knowledge about good solutions, and autonomously absorbs knowledge. CNC machining time was significant shorter by using GA method for NC programming.Research limitations/implications: The system was developed for PC and tested in simulation process. It needs to be tested more in detail in the real manufacturing environment.Practical implications: It is suitable for small and medium-sized companies. Human errors are avoid or at lover level. It is important for engineers in job – shops.Originality/value: The present paper is a contribution to more intelligent systems in production environment. It used genetic based methods to solve engineering problem.
Combined feedforward and feedback control of end milling system
F. Cus,U. Zuperl,J. Balic
Journal of Achievements in Materials and Manufacturing Engineering , 2011,
Abstract: Purpose: Purpose of this paper. An intelligent control system is presented that uses a combination of feedforward and feedback for cutting force control in end milling.Design/methodology/approach: The network is trained by the feedback output that is minimized during training and most control action for disturbance rejection is finally performed by the rapid feedforward action of the network.Findings: The feedback controller corrects for errors caused by external disturbances. The feedforward controller is an artificial neural network (ANN) which approximates the inverse dynamics of the machining process.Research limitations/implications: The dynamic architecture of the neural controller is chosen, and the methods for delay time treatment and training network on line are investigated. The controller was designed and tested using a simulator model of the milling process that includes feed drive model and cutting dynamics simulator.Practical implications: An application to cutting force control in end-milling is used to prove the effectiveness of the control scheme and the experiments shows that the dynamic performance of the cutting force control is greatly improved by this neural combined control system.Originality/value: New combined feedforward and feedback control system of end milling system is developed and tested by many experiments. Also a comprehensive user-friendly software package has been developed to monitor the optimal cutting parameters during machining
Hybrid ANFIS-ants system based optimisation of turning parameters
F. Cus,J. Balic,U. Zuperl
Journal of Achievements in Materials and Manufacturing Engineering , 2009,
Abstract: 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.
Intelligent cutting tool condition monitoring in milling
U. Zuperl,F. Cus,J. Balic
Journal of Achievements in Materials and Manufacturing Engineering , 2011,
Abstract: 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.
Evaluation of shape complexity based on STL data
B. Valentan,T. Brajlih,I. Drstvensek,J. Balic
Journal of Achievements in Materials and Manufacturing Engineering , 2006,
Abstract: Purpose: Purpose of this paper is to present a part complexity, based on basic information of the STL data.Design/methodology/approach: This paper presents a few methods of evaluating the complexity of the shape, based on the parts STL data. Methods vary from very simple based on the number of triangles in STL file and the parts volume, to the more complex mathematical determination based on the relations of the basic STL data.Findings: We discovered that evaluation of shape complexity based only on basic data of STL data gives us some basic view on part complexity.Research limitations/implications: For parts with large block volume/part volume ratio and thinner parts with free form surfaces only the first method is suitable and gives suitable results.Practical implications: The complexity of the shape of a part is an important factor for all manufacturing procedures. When using conventional machining, the parts complexity presents a key factor in determining the optimal way of manufacturing. Also, when using rapid tooling (for example silicon rubber moulding) the complexity of the part determines the parting plane layout and eventual tool construction (inserts, cores, etc.). Even when using certain rapid prototyping procedures, the support material consumption depends highly on the complexity of the part and together with the problem of optimal orientation and position of the part, significantly influences the manufacturing costs. At the end of the article a few test method are presented that try to determine the complexity regarding to the procedure by which the part will be manufactured.Originality/value: Choosing maximum efficient manufacturing processes on base of part complexity is a new perspective in manufacturing, which, properly evolved and complied can cause revolution in manufacturing optimization, especially in hybrid manufacturing processes.
Concept of automatic programming of NC machine for metal plate cutting by genetic algorithm method
B. Vaupotic,M. Kovacic,M. Ficko,J. Balic
Journal of Achievements in Materials and Manufacturing Engineering , 2005,
Abstract: Purpose: In this paper the concept of automatic programs of the NC machine for metal plate cutting by genetic algorithm method has been presented.Design/methodology/approach: The paper was limited to automatic creation of NC programs for two-dimensional cutting of material by means of adaptive heuristic search algorithms.Findings: Automatic creation of NC programs in laser cutting of materials combines the CAD concepts, the recognition of features and creation and optimization of NC programs. The proposed intelligent system is capable to recognize automatically the nesting of products in the layout, to determine the incisions and sequences of cuts forming the laid out products. Position of incisions is determined at the relevant places on the cut. The system is capable to find the shortest path between individual cuts and to record the NC program.Research limitations/implications: It would be appropriate to orient future researches towards conceiving an improved system for three-dimensional cutting with optional determination of positions of incisions, with the capability to sense collisions and with optimization of the speed and acceleration during cutting.Practical implications: The proposed system assures automatic preparation of NC program without NC programer.Originality/value: The proposed concept shows a high degree of universality, efficiency and reliability and it can be simply adapted to other NC-machines.
Optimizing scale factors of the PolyJet rapid prototyping procedure by genetic programming
T. Brajlih,I. Drstvensek,M. Kovacic,J. Balic
Journal of Achievements in Materials and Manufacturing Engineering , 2006,
Abstract: 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.
Canscan — An Algorithm for Automatic Extraction of Canyons
Nebojsa Balic,Barbara Koch
Remote Sensing , 2009, DOI: 10.3390/rs1030197
Abstract: This article introduces a novel algorithm for automatic extraction of canyons which was developed in the Department of the Remote Sensing and Land Information Systems at the University of Freiburg (FELIS). The algorithm detects canyons by means of user-defined dimension parameters and elevation information provided in a Digital Terrain Model (DTM). The extraction procedure is based on the geometric interpretation of canyons through which the input dimension parameters are identified. The dimension parameters are used for identifying cross-sections across DTM on the basis of which canyons are extracted. In addition to the detailed description of the extraction algorithm, this paper includes the results obtained in test regions as well as a thorough discussion.
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