• Design & Manufacturing
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• v.15 no.4
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• pp.24-31
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• 2021

In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection molding conditions and the weight, height, and diameter of the product derived from CAE results were used as the input parameters for learning of the convert model. Also the product qualities of experimental results were used as the output parameters for learning of the convert model. The accuracy of the convert model showed RMSE values of 0.06g, 0.03mm, and 0.03mm in weight, height, and diameter, respectively. As the next step, additional randomly selected conditions were created and CAE analysis was performed. Then, the additional CAE analysis data were converted to similar experimental data through the conversion model. An artificial neural network model was constructed to predict the quality of injection molded product by using converted similar experimental data and injection molding experiment data. The injection molding conditions were used as input parameters for learning of the predicted model and weight, height, and diameter of the product were used as output parameters for learning. As a result of evaluating the performance of the prediction model, the predicted weight, height, and diameter showed RMSE values of 0.11g, 0.03mm, and 0.05mm and in terms of quality criteria of the target product, all of them showed accurate results satisfying the criteria range.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.3
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• pp.441-461
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• 1995

This research proposes an Integrated Product Information Model (IPIM) using STEP (Standard for the Exchange of product model data) for Computer Integrated Manufacturing (CIM) of Concurrent Engineering (CE). IPIM is based on Geometry and Topology (STEP Part 42), Form Feature (STEP Part 48), and Tolerance (STEP Part 48) for representing the integrated information of mechanical parts. For the IPIM, 1) new entities are developed for integration of existing entities, and 2) the existing entities are restructured and modified for a special application protocol. In CIM or CE, the advantages of using IPIM having integrated form of geometry, feature and tolerance are 1) integration of product design, process design and manufacturing sequentially or concurrently. 2) keep the product data consistency, modified by different domain, and 3) automatic data exchange between different application software and different hardware. The prototype system is composed of CAD, Data Probe, DBMS and SDAI (Standard Data Access Interface), and the generated STEP data is stored in a step file of DBMS for other applications.

• Korean Journal of Computational Design and Engineering
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• v.9 no.3
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• pp.260-265
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• 2004

This paper proposed a Web-based training service for product data management by supporting an integrated product data management system, various technical documents. and efficient communication systems. It also supports a general product development process and a consistent product data model that enable participants to experience management of consistent product information during the product development life cycle. The Web based environment of the service also provides participants with a collaborative workplace with other participants and a Web portal for all the components of the service.

• The Journal of Society for e-Business Studies
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• v.1 no.2
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• pp.93-116
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• 1996

Nowadays, there are many efforts to integrate CAD/CAM and other systems. The key of integration is engineering database implementation through the product data definition. In this paper, we suggest the product data definition and their properties for electronic consumer product throughout the requirement analysis for engineering database. Electronic consumer products include electric/electronic parts. as well as mechanical part which mainly compose of machinery. The paper is composed of three parts: the first is analysis about engineering data base. the second is understanding of product data structure and properties. and the last is modeling of product data including static and dynamic characteristics.

• Journal of the Society of Naval Architects of Korea
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• v.30 no.2
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• pp.1-12
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• 1993

The role of product model in CIM environemt(where such heterogenous application programs as CAD, CAE, CAM, Database, and Expert Systems are included) is system integration. Product model manages all the information related to manufacuring activities. This information includes shapes, operation, process, scheduling, quality, and mangement. Product model architecture includes product model kernel, object schema, model manipulation language, and user interface. Objects to be shared are defined using the model manipulation luage and the defintions are saved in the object schema. In this paper, we present the design and implementation of a prototype. In this prototype, application programs for CAPP(Computer Aided Process Planning) are used.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.591-596
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• 2001

To deliver new products to market in a due time, companies often develop their products with numerous partners distributed around the world. Internet technologies can provide a cheap and efficient basis of collaborative product development among distributed partners. This paper provides a framework and its product database model that can support consistent product data during collaborative product development. This framework consists of four components for representing consistent product structure: the product configuration, assembly structure, multiple representations and engineering changes. A product database model realizing the framework is designed and implemented as a system that supports collaborative works in the areas of product design and technical publication. The system enables participating designers and technical publishers to complete their tasks with shared and consistent product data. It also manages the propagation of engineering changes among different representations for individual participants. The Web technologies introduced in this system enable participants to easily access and operate shared product data in a standardized and distributed computing environment.

• Korean Journal of Computational Design and Engineering
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• v.20 no.3
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• pp.254-262
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• 2015

This study proposes an approach to Product Data Management (PDM) education for collaborative product data management, which can support collaborative product development process. This approach introduces social media and a PDM system into a framework for PDM education supported by consistent product development process and product data model. It has been applied to two PDM classes and the result shows that the social media in PDM education can support not only experiences of the collaborative product data management but also interactive and informal communications among instructors and participants using integrated social media with product data during courses.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.65-72
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• 2018

In this paper, we have studied how to reuse common data link software by applying software product line engineering. Existing common data link software performed different stages of design, implementation, and testing without sharing the accumulated knowledge of different developers. In this situation, developers agreed that sharing the assets of each project and reusing the previously developed software would save human and time costs. Even with the initial difficulties, the common Data Link is a continually proposed project in the defense industry, so we decided to build a product line. The common data link software can be divided into two domains. Among them, the initial feature model for the GUI software was constructed, and the following procedure was studied. Through this, we propose a plan to build a product line for core assets and reuse them in newly developed projects.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.785-790
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• 2006

In RFID-based SCM, The traceability and product information is the important target data. In this paper, efficient items traceability model and the integrated model of the product between RFID network and GDS(Global Data Synchronization) network are studied. Information consists of the dynamic data generated from RFID network and static data generated from GDS Network. The integrated model will provide the interoperability between 2 kinds of networks.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.