A supervised machine learning (ML) based computational methodology for the design of particulate multifunctional composite materials with desired thermal conductivity (TC) is presented. The design variables are physical descriptors of the material microstructure that directly link microstructure to the material's properties. A sufficiently large and uniformly sampled database was generated based on the Sobol sequence. Microstructures were realized using an efficient dense packing algorithm, and the TCs were obtained using our previously developed Fast Fourier Transform (FFT) homogenization method. Our optimized ML method is trained over the generated database and establishes the complex relationship between the structure and properties. Finally, the application of the trained ML model in the inverse design of a new class of composite materials, liquid metal (LM) elastomer, with desired TC is discussed. The results show that the surrogate model is accurate in predicting the microstructure behavior with respect to high-fidelity FFT simulations, and inverse design is robust in finding microstructure parameters according to case studies.
翻译:设计变量是将微结构与材料的特性直接联系起来的材料微结构的物理描述器。根据Sobol序列生成了一个足够大和统一的抽样数据库。微型结构是使用高效的密集包装算法实现的,并且利用我们以前开发的快速Fourier变异(FFT)同质化方法获得的。我们优化的ML方法经过了对生成数据库的培训,确定了结构与属性之间的复杂关系。最后,经过培训的ML模型在新型复合材料、液体金属(LM)弹性体和理想的TC的反向设计中应用。结果显示,在预测高纤维FFT模拟的微结构行为方面,套样模型是准确的,反面设计在根据案例研究找到微结构参数方面是有力的。