• Korean Journal of Computational Design and Engineering
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• v.8 no.4
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• pp.222-230
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• 2003

Artificial intelligence has been used in the configuration design stage of high-speed train. The traction system of a high-speed train is composed of transformers, motor blocks, and traction motors of which locations and number in the trainset should be determined in the early stage of the train conceptual design. Components of the traction system are heavy parts in the train, so it gives strong influence to the top speeds and overall train configuration of high-speed trains. Top speeds have been predicted using the neural network with the associated data of the traction system. The neural networks have been learned with data sets of many commercially operated high-speed trains, and the predicted results have been compared with the actual values. The configuration design of the train set of a high-speed train determines the basic specification of the train and layout of the traction system. The neural networks is a useful design tool when there is not sufficient data for the configuration design and we need to use the existing data of other train for the prediction of trainset in development.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.3
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• pp.230-238
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• 2003

The configuration design of a mechanical product can be efficiently performed when it is based on the functional modeling. There are methodologies, which decompose function from the abstract level to the concrete level and match the functions to physical parts. But it is difficult to carry out an innovative design when the function is matched only to a pre-detined part. This paper describes the configuration design process of a mechanical product with a design expert system, which uses function taxonomy and TRIZ theory. The expert system can propose a functional modeling of a new part. which is not in the existing parts list. The abstraction levels of design knowledge are introduced, which describe the operation of mechanical product in the levels of abstraction. This is the theoretical background of using knowledge of function and TRIZ for configuration design. The expert system is adequate to control this design knowledge. which expresses knowledge of functional modeling, mapping rules between functions and parts, selection of parts, and TRIZ theory. The hierarchy of functions and machine parts are properly expressed by classes and objects in the expert system. A design expert system has been implemented for the configuration design of a train bogie, and a new brake system of the bogie is introduced with the aid of TRIZ's 30 function groups.

• Korean Journal of Computational Design and Engineering
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• v.5 no.4
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• pp.380-392
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• 2000

This paper proposes a qualitative reasoning approach for the spatial configuration of mechanisms that could be applied in the early phase of the conceptual design. The spatial configuration problem addressed in this paper involves the relative direction and position between the input and output motion, and the orientation of the constituent primitive mechanisms of a mechanism. The knowledge of spatial configuration of a primitive mechanism is represented in a matrix form called spatial configuration matrix. This matrix provides a compact and convenient representation scheme for the spatial knowledge, and facilitates the manipulation of the relevant spatial knowledge. Using this spatial knowledge of the constituent primitive mechanisms, the overall configuration of a mechanism is described and identified by a spatial configuration state matrix. This matrix is obtained by using a qualitative reasoning method based on sign algebra and is used to represent the qualitative behavior of the mechanism. The matrix-based representation scheme allows handling the involved spatial knowledge simultaneously and the proposed reasoning method enables the designer to predict the spatial behavior of a mechanism without knowing specific dimension of the components of the mechanism.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.1
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• pp.229-239
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• 2003

The design process of machine tools is regarded as a sequential, discrete, and inefficient works as it requires various kinds of design tools and many working hours. This paper describes an integrated design system embedding a design methodology that can support efficiently and systematically the conceptual structural design of machine tools. The system is a knowledge-based design system and has four machine-tool-specific functional modules including configuration design, configuration analysis, structure design, and structural analysis support module. Through the configuration design and analysis module, a machine configuration appropriate for design requirements is selected, and then the arrangement of ribs fer each structural part is decided in the structure design module. Also, the structural analysis support module is used to evaluate design result by utilizing structural analysis software, ANSYS. The system is applied to design of a tapping machine, and shows that the machine structure can be designed fast and conveniently by processing each design step interactively.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.398-403
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• 2000

The design of multi-stage gear drives is a time-consuming process that includes additional design problems, which are not considered in the design of single-stage gear drives. In the previous research works, the authors have proposed a new algorithm to design multi-stage gear drives at the preliminary design phase. The proposed design algorithm automates the design process by integrating the dimensional design and the configuration design process. In the configuration design process, the positions of gears and shafts are determined by minimizing the geometrical volume (size) of a gearbox. However, various types of spatial constraints should be satisfied in practical design situation. To locate input and output shaft in specified positions is the typical example of such problems. In this paper, the authors show the formulations of spatial constraints applied to the design of four-stage gear drives. The design solution shows considerably good results, and the design system is confirmed to be readily applicable to practical design situation.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.1
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• pp.107-113
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• 2021

Plant Configuration Management(CM) is an activity to maintain consistency of design requirements, physical configuration, facility configuration information throughout the life cycle of the plant by systematically managing changes that occur during the plant design and operation process. Conformity between information must be ensured not only in the design stage, but also in the case of design changes in the operation and maintenance stages, and thus a computer system capable of efficiently managing them is required. In particular, in consideration of an application to small and medium-sized domestic plants, a computer system that can support configuration management at a low cost is needed. Accordingly, in this study, a configuration management system has been developed to support the management of plant design information and change procedures in the operation stage of small and medium-sized plants. Here, a case for quantitatively evaluating the effectiveness of the developed system is described.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.110-117
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• 1998

A continuum-based configuration design sensitivity analysis method is developed for kinematically driven mechanical systems. The configuration design variable for mechanical systems is defined. The 3-1-3 Euler angle is employed as the orientation design variable. Kinematic admissibility conditions of configuration design change. Direct differentiation method is used to derive the governing equations of the design sensitivity. Numerical examples are presented to demonstrate the validity and effectiveness of the proposed method.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.464-469
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• 1999

Recently, the need for designing multi-stage gear drive has been increased as the hear drives are used more in the applications with high-speed and small volume. The design of multi-stage gear drives includes not only dimensional design but also configuration design of various machine elements. Until now, however, the researches on the design of gear drives are mainly focused on the single-stage gear drives and the design practices for multi-stage gear drives, especially in configuration design activity, mainly depend on the experiences and 'sense' of the designer by trial and error. We propose a design algorithm to automate the dimension design and the configuration design of multi-stage gear drives. The design process consists of four steps. The number of stage should be determined in the first step. In second step, the gear ratios of each reduction stage are determined using random search, and the ratios are basic input for the dimension design of gears, which is performed by the exhaustive search in third step. The designs of gears are guaranteed by the pitting resistance and bending strength rating practices by AGMA rating formulas. In configuration design, the positions of gears are determined to minimize the volume of gearbox using simulated annealing algorithm. The effectiveness of the algorithm is assured by the design example of a 4-stage gear drive.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.2
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• pp.65-72
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• 2001

The design of multi-stage gear drives is a time-consuming process, since on includes more complicated problems, which are not considered in the design of single-stage gear drives. The designer has th determine the number of reduction stages and the gear ratios of each reduction state. In addition, the design problems include not only the dimensional design but also the configuration design of gear drive elements. There is no definite rule and principle for these types of design problems. Thus the design practices largely depend on the sense and the experiences of the designer , and consequently result in undesirable design solution. We propose a new generalized design algorithm to support the designer at the preliminary design phase of multi-stage gear drives. The proposed design algorithm automates the design process by integrating the dimensional design and the configuration design process. The algorithm consists of four steps. In the first step, a designer determines the number of reduction stage. In the second step. gear ratios se chosen by using the random search method. In the third step, the values of basic design parameter are chosen by using the generate and test method. Then, the values of other dimension, such ad pitch diameter, outer diameter, and face width, are calculated for the configuration design in the final step. The strength and durability of a gear is guaranteed by the bending strength and the pitting resistance rating practices by using the AGMA rating formulas. In the final step, the configuration design is carried out b using the simulated annealing algorithm. The positions of gears and shafts are determined to minimize the geometrical volume(size) of a gearbox, while satisfying spatial constraints between them. These steps are carried out iteratively until a desirable solution is acquired. The propose design algorithm has been applied to the preliminary design of four-stage gear drives in order to validate the availability. The design solution have shown considerably good results in both aspects of the dimensional and the configuration design.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.780-787
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• 2017

The propellant grain configuration is a design variable that determines the shape and performance of a solid rocket motor. Grain configuration variables have complicated effects on the motor performance; so the global optimization problem has to be solved in order to design the configuration variables. The grain performance has been analyzed by means of the grain burn-back and internal ballistic analysis, and the optimization technique searches for the configuration variables that satisfy the requirements. The deterministic and stochastic optimization techniques have been applied for the grain optimization, but the results are imperfect. In this study, the optimization design of the configuration variables has been performed using the hybrid optimization technique, which combines those two techniques. As a result, the hybrid optimization technique has proved to be efficient for the grain optimization design.