A
physical information machine learning design method of a multi-component composite high-thermal-
conductivity filler belongs to the technical field of composite high-thermal-
conductivity filler design, and comprises the following steps: S1, establishing a material intrinsic attribute
data set; s2, establishing a three-dimensional space closest packing model; s3, setting boundary constraint conditions; s4, optimizing a
machine learning
algorithm; and S5, carrying out proportion conversion. According to the method, an existing
machine learning technology is utilized, a comprehensive
data set is constructed, a
machine learning algorithm is applied, the
porosity is reduced and heat conduction paths are increased through accurate material particle
size matching, and different three-dimensional space closest packing
theoretical models are correspondingly selected according to material intrinsic attributes; therefore, on the basis of optimizing the stacking structure of the optimal filler of the heat-conducting
polymer composite material, forming an efficient heat-conducting path and keeping the
processing performance of the heat-conducting
polymer composite material, the design and manufacturing processes are simplified, the production efficiency is improved, and powerful
technical support is provided for effective heat dissipation and reliable and stable work of
electronic equipment.