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논문 기본 정보

자료유형
학술저널
저자정보
이광기 (브이피코리아) 한승호 (동아대학교)
저널정보
Korean Society for Precision Engineering Journal of the Korean Society for Precision Engineering 한국정밀공학회지 Vol.28 No.4
발행연도
2011.4
수록면
482 - 489 (8page)

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초록· 키워드

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In practical design process, designer needs to find an optimal solution by using full factorial discrete combination, rather than by using optimization algorithm considering continuous design variables. So, ANOVA(Analysis of Variance) based on an orthogonal array, i.e. Taguchi method, has been widely used in most parts of industry area. However, the Taguchi method is limited for the shape optimization by using CAE, because the multi-level and multi-objective optimization can‘t be carried out simultaneously. In this study, a combined method was proposed taking into account of multi-level computational orthogonal array and TOPSIS(Technique for Order preference by Similarity to Ideal Solution), which is known as a classical method of multiple attribute decision making and enables to solve various decision making or selection problems in an aspect of multi-objective optimization. The proposed method was applied to a case study of the multi-level shape optimization of lower arm used to automobile parts, and the design space was explored via an efficient application of the related CAE tools. The multi-level shape optimization was performed sequentially by applying both of the neural network model generated from seven-level four-factor computational orthogonal array and the TOPSIS. The weight and maximum stress of the lower arm, as the objective functions for the multi-level shape optimization, showed an improvement of 0.07% and 17.89%, respectively. In addition, the number of CAE carried out for the shape optimization was only 55 times in comparison to full factorial method necessary to 2,401 times.

목차

1. 서론
2. CAE 기반 자동차 로워암의 응력해석
3. 다수준 직교배열 및 TOPSIS 를 적용한 신경망 기반 근사모델의 구축
4. 로워암의 다수준 형상최적설계
5. 결론
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