• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.221-229
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making fer the conceptual product design and the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA fur a various range of design concepts need the new approach fer the environmental analysis. This paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes into impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. The training is generalized by using product attributes for an ID in a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines fer the design of environmentally conscious products in conceptual design phase.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.127-141
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• 1996

In this paper, we propose a 7-phase life cycle model which applies to Computer Integrated Manufacturing systems. The model emphasizes product design and manufacturing design activities of CIM to secure the critical success factors of CIM systems such as high quality, adaptability, productivity, and flexibility. It is argued that the product design aspect would be divided into three phases-conceptual design, embodiment design, and detialed design. The conceptual design phase is to build a conceptual model of the product based on requirements and specifications which reflect "the voice of the customer". THe embodiment design phase utilizes specific design tools such as DFM, CAE, and CAD, and results in a concrete model of the product and parts. The detailed design phase is to crete a working prototype of the product and design tools such as DFA. CAD and CAM are employed in this phase. The output of the product design activity is to be the input for the manufacturing design activity. Using the proposed model, one can effectively and efficiently manufacture a high-quality, low-cost product with short delivery time, and above all achieve customer'ssatisfaction.isfaction.

• Proceedings of the Korea Society of Design Studies Conference
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• 2001.10a
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• pp.84.1-84
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• 2001

• Journal of the Korean Society of Systems Engineering
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• v.3 no.2
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• pp.39-48
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• 2007

In this study, an overall product design process will be presented by using the Analytic Hierarchy Process(AHP) and robust design. From the conceptual design stage, the logical methods are used to select the appropriate concepts satisfying the customer requirements and the other conditions. The next phase is the embodiment design phase in which the deterministic and robust design approach are used to obtain the improvement in product design. Typically, this approach is applied for developing the simple bookshelf design. The results show the efficient approach which can be supported to develop the new product.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.780-783
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• 2002

This paper proposes an approximate LCA methodology fur the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes Into impact driver (ID) index. The relationship Is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then an artificial neural network model is developed to predict an approximate LCA of grouping products in conceptual design stage. The training is generalized by using identified product attributes for an ID In a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give an approximate LCA results for design concepts.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.540-545
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• 2002

The tight integration of the part design and process planning is very effective to high quality product development and cost effective manufacturing. Moreover, the integration in the early design stage, that is, the integration of the conceptual design and the conceptual process planning may take a big impact with the forecasting the alternative of the design and manufacturing. In this paper, the real field parts are studied about the relations among the Feature, Function, and Manufacturing Process taking the style of reverse engineering method, to found the base of the systematic computer system for the integrated product design and manufacturing process planning.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.3
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• pp.56-65
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• 2006

It is well known and widely accepted that the conceptual design is the most influential step in the design process of a product or a system and that about 75% of the life cycle cost is committed as the results of this stage. The purpose of this paper is to present and demonstrate the step of ICDM(Integrated, Customer Driven, Conceptual Design Method) for the development of a ground launcher system, TEL(Transporter, Erector and Launcher). The results of the study show the effectiveness of the method during the conceptual design phase of new complex systems or high-tech products.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.85-94
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• 2004

As over 70% of the total life cycle cost (LCC) of a product is committed at the early design stage, designers are in an important position to substantially reduce the LCC of the products they design by giving due to life cycle implications of their design decisions. During early design stages, there may be competing concepts with dramatic differences. In addition, the detailed information is scarce and decisions must be made quickly. Thus, both the overhead in developing parametric LCC models fur a wide range of concepts, and the lack of detailed information make the application of traditional LCC models impractical. A different approach is needed, because a traditional LCC method is to be incorporated in the very early design stages. This paper explores an approximate method for providing the preliminary LCC, Learning algorithms trained to use the known characteristics of existing products might allow the LCC of new products to be approximated quickly during the conceptual design phase without the overhead of defining new LCC models. Artificial neural networks are trained to generalize product attributes and LCC data from pre-existing LCC studies. Then the product designers query the trained artificial model with new high-level product attribute data to quickly obtain an LCC for a new product concept. Foundations fur the learning LCC approach are established, and then an application is provided.

• Journal of Mechanical Science and Technology
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• v.17 no.12
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• pp.1969-1976
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making for the product concepts, and the best alternative can be selected based on its estimated LCA and benefits. Both the lack of detailed information and time for a full LCA for a various range of design concepts need a new approach for the environmental analysis. This paper explores a new approximate LCA methodology for the product concepts by grouping products according to their environmental characteristics and by mapping product attributes into environmental impact driver (EID) index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then, a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for newly designed products. The training is generalized by using product attributes for an EID in a group as well as another product attributes for the other EIDs in other groups. The neural network model with back propagation algorithm is used, and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines for the design of environmentally conscious products in conceptual design phase.

• Journal of the Ergonomics Society of Korea
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• v.12 no.2
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• pp.29-43
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• 1993

This paper presents a conceptual approach to convert customer requirements expressed in ordinary language into a form of qualitative and quantitative functions for developing new products. This approach attempts to combine the concepts of the value engineering and the Kansei engineering. It emphasizes that customer require- ments should be interpreted and reflected on the design of new product. Specific are discussed for extracting subjective requirements and transforming them into qualitative and quantitative functions for product design. This approach is expected to provide the product designer with a systematic efficient tool for incorporating subjective requirements into a product design.