• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.632-635
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• 2002

There are more than five dozen different RP(rapid prototyping) systems in the world and they are fairly expensive. All those systems have different capabilities and requirements in that each of them gives different tolerance, application field and part strength, etc. This situation may cause a problem of selecting an appropriate RP system. This paper presents an expert system, utilizing an algorithm that is composed up of rules to derive recommendations and answers to queries of the RP users. The expert system incorporates RP machines commercially available and adopts multi-selection criteria, namely, machine price, accuracy, build size, adopted process, etc. In the expert system, forward reasoning method is adopted and external spreadsheet for sub-data of the RP systems is used. The rules and knowledge are obtained from interviews and discussions with RP vendors and users, appropriate research publications and other reference materials.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.3
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• pp.87-95
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• 2008

The purpose of this study is to provide a decision support to select an appropriate rapid prototyping(RP) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model for molding, material property, build time and part cost that greatly affect the performance of RP machines. However, the selection of a RP is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate RP machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify RP machines that the users consider. After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of RP machines.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.5-8
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• 2003

This paper presents a methodology to be able to select an appropriate RP system that suits the end use of a part. Evaluation factors used in process selection include major attributes such as accuracy, roughness, strength, elongation, part cost and build time that greatly affect the performance of RP systems. Crisp values such as accuracy and surface roughness are obtained with a new test part developed. The test part is designed with conjoint analysis to reflect users' preference. The part cost and build time that have approximate ranges due to cost and many variable parameters are presented by linguistic values that can be described with triangular fuzzy numbers. Based on the evaluation values obtained, an appropriate RP process for a specific part application is selected by using the modified TOPSIS(Technique of Order Preference by Similarity to Ideal Solution) method. It uses crisp data as well as linguistic variables, and each weight on the alternatives is assigned by using pair-wise comparison matrix. The ranking order helps the decision making of the selection of RP systems.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.22-25
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• 2003

It is usually difficult for a single user to have all the essential knowledge on various Rapid Prototyping processes and techniques. It is therefore necessary to capture knowledge and experience of users of expert level into a decision-support system which provides quicker and more interactive way to select proper RP process and/or machine. rather than reading reports on benchmarking studies and comparing tables and graphs. In this paper two algorithms are presented, which may be used in such a decision-support system. together with its applications. The one is an extended PRES(Project Evaluation and Selection) algorithm which applies weighting factors of each attribute. The other is a LCE(Linear Confidence Equation) algorithm which is proposed to apply user's input requirements as well as weighting factors.

• Journal of Mechanical Science and Technology
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• v.20 no.1
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• pp.19-28
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• 2006

This paper describes rapid prototyping (RP) and reverse engineering (RE) application for orthopedic surgery planning to improve the efficiency and accuracy of the orthopedic surgery. Using the symmetrical characteristics of the human body, CAD data of undamaged bone of the injured area are generated from a mirror transformation of undamaged bone data for the uninjured area. The physical model before the injury is manufactured from Poly jet RP process. The surgical plan, including the selection of the proper implant, pre-forming of the implant and decision of fixation positions, etc., is determined by a physical simulation using the physical model. In order to examine the applicability and efficiency of the surgical planning technology, two case studies, such as a distal tibia comminuted fracture and an iliac wing fracture of pelvis, are carried out. From the results of the examination, it has been shown that the RP and RE can be applied to orthopedic surgical planning and can be an efficient surgical tool.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.11
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• pp.2409-2414
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• 2002

The surfaces of prototypes become rough due to the stair-stepping which is the inevitable phenomenon in the Rapid Prototypes are not used only for the verification of feature. The grinding, coating, or the composition of them is a main operation in post-processing in which lots of costs and long build time are needed. The solution is proposed to increase the efficiency of rapid prototyping by minimizing or removing the composition of them is a main operation in post-processing in which lots of costs and long build time are needed. the solution is proposed to increase the efficiency of rapid prototyping by minimizing or removing the regions for post-processing. the factors to cause the surface roughness and their effects are analyzed through the experiments. Software modules are developed to predict the surface roughness of each face in the prototyping with the result. An experimental compensation method is developed to apply the modules to various RP equipments, materials and build styles. The build direction is searched with use of genetic algorithm to maximize the total areas of the surface of which roughness is better than the user-defined value.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.433-438
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• 2004

The rapid machining of prototypes plays an important role in product process. Rapid Prototyping(RP) is the widespread technology to produce prototype. But, it have many problems such as shrinkage, deformation and formation occurred by hardening of resin and stair shaping, On the contrary, high speed machining(HSM) technology has many advantages such as good quality, low cost and rapid machining time. Moreover, it is possible to use the material of original product. This paper presents manufacture of trial product by HSM and optimization of machining condition for high productivity in the view of manufacturing time and average error. For example, propeller is machined by the surface machining of thin surface parts. Experiments are designed of machining conditions by Latin Square method and machining condition is optimized and selected by ANOVA

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.37-40
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• 2001

This paper describes the selection and evaluation of RP processes. Major rapid prototyping processes such as SLS, SLA, FDM and LOM, which are wide spread in use are selected. A test part, which includes various primitives, is designed in order to evaluate these RP processes. Measurement of the test part is automated by using a CMN program. To visualize and analyze measured data, Microsoft Access and Visual C++ are used. Also, from measured data obtained, TOPSIS, one of the decision making methods, and Shannon Entropy is used to select an appropriate RP process for specific application.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.137-140
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• 1997

In general, stereolithography parts is not suitable for master pattern. Because of its bad surface roughness. Therefore, To reduce roughness it requires post-process that is depending on user skill and takes long time to do. This study aims to develop an expert system which can select an optimal build orientation, reduce roughness and shorten post-processing time. Genetic Algorithm was introduced for optimization. A simplified computation model was developed for real-time response. For accurate roughness estimation, mterpolation of experimental data was implemented.