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Development of Hierarchical Machine Learning for the Modeling and Design of Complex Material Systems
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Development of Hierarchical Machine Learning for the Modeling and Design of Complex Material Systems. |
| 개인저자 | Menon, Aditya. |
| 단체저자명 | Carnegie Mellon University. Materials Science and Engineering. |
| 발행사항 | [S.l.]: Carnegie Mellon University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 181 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-03B. Dissertation Abstract International |
| ISBN | 9781088385654 |
| 학위논문주기 | Thesis (Ph.D.)--Carnegie Mellon University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Advisor: Washburn, Newell R. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | The overarching objective of this study was to develop a methodology for optimization of system responses in complex material systems with small datasets utilizing machine learning/statistical techniques with domain specific system data. These systems are ubiquitous in various large-scale materials industries like detergents, food packaging, agrochemicals, cement to small scale and emerging sectors like 3D printing etc. The algorithm developed in this research is referred to as Hierarchical Machine Learning (HML). As part of this study, disparate material systems have been optimized and validated in three major categories of system variables-molecular design, formulation and processing. Molecular design was validated using HML by (a) developing a novel superplasticizer for a non-setting cement model (MgO) and then (b) developing a novel superplasticizer for a Metakaolin-Portland cement blend to improve rheological behavior (slump). Process optimization was validated using HML on a silicone 3D printing system by improving print speed and diversity without degradation of print fidelity. Formulations optimization was validated using HML on a system of polyurethane films with varying polyols and diisocyanate reactants and varying NCO: OH index for prediction of mechanical responses, strain at break, stress at break and Tan 灌 with a further purpose of enabling prediction for substituted reactants and categorical responses. Based on these studies, it is shown that HML can be used to gain physical insight from a material system with a small and high-quality dataset while enhancing the predictive capability of the system. This methodology can then be extended to optimize the system for desired complex responses. |
| 일반주제명 | Engineering. Polymer chemistry. Materials science. |
| 언어 | 영어 |
| 바로가기 | 
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