DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Ryu, Seunghwa | - |
dc.contributor.advisor | 유승화 | - |
dc.contributor.author | Jung, Ji-Young | - |
dc.date.accessioned | 2023-06-21T19:33:29Z | - |
dc.date.available | 2023-06-21T19:33:29Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1030360&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/307885 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 기계공학과, 2023.2,[xiv, 197 p. :] | - |
dc.description.abstract | Material development and process optimization are essential for product development in the manufacturing industry. Homogenization theory can efficiently predict the effective behavior of the composites using mathematical assumptions. In this thesis, two methods are proposed to predict the effective behavior of composites. First, an adaptive incrementally affine method improved from the previous homogenization method is proposed and verified for viscoelastic-viscoplastic composites. Second, a methodology that can efficiently train a deep neural network model predicting the effective behavior of composites is proposed by combining homogenization theory and transfer learning. On the other hand, process optimization is essential to improve productivity and quality of products. In particular, injection molding is the most commonly used process for plastic products. In this thesis, frameworks for optimizing the injection molding process are proposed using multiobjective Bayesian optimization and constrained generative inverse design networks. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Homogenization▼aComposites▼aTransfer learning▼aDNN▼aProcess optimization▼aMachine learning | - |
dc.subject | 균질화▼a복합재▼a전이학습▼a심층신경망▼a공정 최적화▼a기계학습 | - |
dc.title | Effective behavior analysis of composite using homogenization and transfer learning and process optimization for injection molding based on machine learning | - |
dc.title.alternative | 균질화 이론과 전이학습을 활용한 복합재 유효 거동 해석과 기계학습 기반 사출성형 공정 최적화 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :기계공학과, | - |
dc.contributor.alternativeauthor | 정지영 | - |
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