عنوان

Structure-Property Prediction for Magnetorheological Elastomer Using Machine Learning Approaches

شماره

TLpq2299003558

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Structure-Property Prediction for Magnetorheological Elastomer Using Machine Learning Approaches

نام عام مواد

[Thesis]

نام نخستين پديدآور

Feng, Shengwei

نام ساير پديدآوران

Sun, Lizhi

نام ناشر، پخش کننده و غيره

University of California, Irvine

تاریخ نشرو بخش و غیره

2019

نام خاص و کميت اثر

99

جزئيات پايان نامه و نوع درجه آن

M.S.

کسي که مدرک را اعطا کرده

University of California, Irvine

امتياز متن

2019

متن يادداشت

Magnetorheological elastomer (MRE) is a rubbery composite material filled with micron-sized ferromagnetic particles whose mechanical properties can be tailored by the application of external magnetic fields. Due to its magnetic and mechanical coupling effect, MRE is increasingly used in the field of engineering. Capturing the responses of MRE is essential for materials modeling and can be reached either by the physics-based finite element modeling or data-based artificial intelligence modeling. In this thesis, machine learning-based data-driven models are built to discover the structure-property linkages of MRE. The proposed method employs a pre-trained Convolutional Neural Network (CNN) and also an artificial neural network (ANN) to evaluate the critical features of the material microstructures that lead to precise predictions for the critical mechanical properties of MRE. It has been proven that these approaches can make compelling predictions while dramatically reduce the time needed for the calculation process. With low computation cost, the machine learning models also exhibit great potential in microstructure optimization.

موضوع مستند نشده

Civil engineering

موضوع مستند نشده

Engineering

مستند نام اشخاص تاييد نشده

Feng, Shengwei

مستند نام اشخاص تاييد نشده

Sun, Lizhi

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y