Wyniki wyszukiwania - Biblioteka Nauki

1

Knowledge management based process planning system

100%

Gecevska V. , Cus F.

Journal of Machine Engineering

|

2009

|

tom Vol. 9, No. 4

107--120

EN

Process planning knowledge (PPK) is one of the most important knowledge in production manufacturing enterprise. The traditional method of organizing data into knowledge relies on manual analysis and interpretation. This paper analyzes the source and composing of process planning knowledge and state of arts on process planning discovery in production manufacturing enterprise. On the basis of the application of computer aided process planning (CAPP) system in mechanical manufacturing enterprise, the concept of process planning information model (PPIM) is proposed based on process planning databases. This paper provides a CAPP database developed in own research, clarifying how PPK and PPIM in CAPP database are related both to each other and to related fields, the technology database of process planning knowledge discovery is modeled based on object-oriented model-driven technology, and the process planning knowledge discovery script is designed.

2

Optimization software optimad for determinatioin of machining parameters and modeling of cutting process

100%

Gecevska V. , Kuzinovski M.

Systems : journal of transdisciplinary systems science

|

2004

|

tom Vol. 9, No. sp. I

16--21

EN

This paper proposes a methodology for optimization and analyzing machining conditions by optimal determination of the cutting parameters in multipass NC machining operation. The proposed approach for selecting optimal cutting parameters, where the formulation involves the use of empirical relations, is considered. A new optimization approach is proposed which uses a possibility formulation of the classical optimization problem and optimizes the resulting mathematical model using a classic (deterministic) and heuristic (genetic algorithm) method.

3

High-speed milling of light metals

80%

Cus F. , Zuperl U. , Gecevska V.

Journal of Achievements in Materials and Manufacturing Engineering

|

2007

|

tom Vol. 20, nr 1-2

419-422

EN

Purpose: Purpose of this paper: Introduction applicability of high-speed cutting of light metals is presented in this paper. Design/methodology/approach: HSC is the result of numerous technical advances ensuring that milling has become faster than conventional milling and has gained importance as a cutting process. The advantages of the HSC milling are higher productivity owing to the reduction of machining times increase of the flow time of production, reduction of the number of technological operations, increase of the surface quality and longer service life of tools. The machining conditions for execution of the HSC (36000 min-1 and feeding 20 m/min) require modernly built machine tools to meet those machining conditions. Findings: Continuous development of new materials is more dynamical, particularly, in the automobile, aircraft and electronic industry and in the manufacture of various mechanical parts. Also the achievements in the area of building of machines and tools, ensuring high cutting speeds (highly efficient machining) have contributed to development of the process. Research limitations/implications: High quality of the surfaces, the quality of this so-called HSC milling can be compared to grinding. Practical implications: High-speed milling of light metals from aluminium and magnesium is more and more frequently used in practice. This result is high quality of the surface and shorter machining times. In some cases when machining by grinding is specified, the latter is omitted. Originality/value: The applicability of high-speed milling has proved to be successfull, when aluminium and magnesium alloying materials are machined.

4

High speed end-milling optimisation using Particle Swarm Intelligence

80%

Cus F. , Zuperl U. , Gecevska V.

Journal of Achievements in Materials and Manufacturing Engineering

|

2007

|

tom Vol. 22, nr 2

75-78

EN

Purpose: In this paper, Particle Swarm Optimization (PSO), which is a recently developed evolutionary technique, is used to efficiently optimize machining parameters simultaneously in high-speed milling processes where multiple conflicting objectives are present. Design/methodology/approach: selection of machining parameters is an important step in process planning therefore a new methodology based on PSO is developed to optimize machining conditions. Artificial neural network simulation model (ANN) for milling operation is established with respect to maximum production rate, subject to a set of practical machining constraints. An ANN predictive model is used to predict cutting forces during machining and PSO algorithm is used to obtain optimum cutting speed and feed rate. Findings: The simulation results show that compared with genetic algorithms (GA) and simulated annealing (SA), the proposed algorithm can improve the quality of the solution while speeding up the convergence process. PSO is proved to be an efficient optimization algorithm. Research limitations/implications: Machining time reductions of up to 30% are observed. In addition, the new technique is found to be efficient and robust. Practical implications: The results showed that integrated system of neural networks and swarm intelligence is an effective method for solving multi-objective optimization problems. The high accuracy of results within a wide range of machining parameters indicates that the system can be practically applied in industry. Originality/value: An algorithm for PSO is developed and used to robustly and efficiently find the optimum machining conditions in end-milling. The new computational technique has several advantages and benefits is suitable for use combined with ANN based models where no explicit relation between imputs and outputs is available. This research opens the door for a new class of optimization techniques which are based on Evolution Computation in the area of machining.

5

High-speed milling of light metals

80%

Cus F. , Zuperl U. , Gecevska V.

Journal of Achievements in Materials and Manufacturing Engineering

|

2007

|

tom Vol. 24, nr 1

357-364

EN

Purpose: of this paper: Introduction applicability of high-speed cutting of light metals is presented in this paper. Design/methodology/approach: HSC is the result of numerous technical advances ensuring that milling has become faster than conventional milling and has gained importance as a cutting process. The advantages of the HSC milling are higher productivity owing to the reduction of machining times increase of the flow time of production, reduction of the number of technological operations, increase of the surface quality and longer service life of tools. The machining conditions for execution of the HSC (36000 min to the -1 and feeding 20 m/min) require modernly built machine tools to meet those machining conditions. Findings: Continuous development of new materials is more and more dynamical, particularly, in the automobile, aircraft and electronic industry and in the manufacture of various mechanical parts. Also the achievements in the area of building of machines and tools, ensuring high cutting speeds (highly efficient machining) have contributed to development of the process. Research limitations/implications: High quality of the surfaces, the quality of this so-called HSC milling can be compared to grinding. Practical implications: High-speed milling of light metals from aluminium and magnesium is more and more frequently used in practice. This result is high quality of the surface and shorter machining times. In some cases when machining by grinding is specified, the latter is omitted. Originality/value: The applicability of high-speed milling has proved to be successful, when aluminum and magnesium alloying materials are machined.

6

Intelligent approach for optimal modeling of manufacturing systems

70%

Gecevska V. , Cus F. , Lombardi F. , Dukowski V. , Kuzinowski M.

Journal of Achievements in Materials and Manufacturing Engineering

|

2006

|

tom Vol. 14, nr 1-2

97--103

EN

Purpose: This paper proposes a methodology for analysis and modeling of machining conditions by optimal determination of the cutting parameters in multi-pass NC machining operations. Design/methodology/approach: This paper proposes optimal determination of the cutting parameters by using a deterministic method (DM) and a genetic algorithm (GA). In the research, it is created the complex mathematical model for design of the cutting condition for machining process. In next phase, it is created a numerical algorithm for optimization and its developed software called OPTIMAD (Optimization of Milling and Drilling), by using DM. Also, it is created software, caled GAMO (Genetic Algorithm for Machining Operation), as a GA program modul based of the elementary pseudo-code for GA, with using the MatLAB program language and C++ developed rutines. Findings: Modeling of optimal cutting parameters, as a part of process planning, enables generating of manufacturing data and knowledge representation in machining process plan. Verification of optimized cutting parameters in real machining condition has done confirmation for design of cutting parameters by virtual modelling, using optimization methodologies OPTIMAD and GAMO. Research limitations/implications: The optimization approach is proposed and its uses optimization of mathematical model using a classic and heuristic methods. In this research, GA based optimization method and deterministic optimization method are developed and there implementations into real manufacturing process are analyzed. Practical implications: Use of proposed approach resulted in improved productivity and efficiency of machining process where the cutting conditions are designed by OPTIMAD and GAMO softwares. In the future, this results will be integrated in computer system for process planning. Originality/value: The paper describes a method for eliminating the need for using the extensive user intervention in CAM processes, during determination of cutting parameters.