Thermal conductivity enhancement of asbestos reinforced phenolic resin frictional composites by Abaqus-Python

Abstract

The main problem of using organic frictional materials as brake lining materials is to solve the problem of stability of friction coefficient and wear rate; to prevent the dramatic temperature rise during braking and thermal decomposition of the resin matrix. To solve this problem, it is common to add materials with good thermal conductivity such as graphite, but at the same time there is disadvantage of low friction coefficient. Thus, to maintain a constant friction coefficient and also to improve the thermal conductivity of the material sufficiently, it is necessary to determine not only the optimum composition of the other fillers but also the binder and reinforcement components of the friction composite. Recently, the wide application of various engineering analysis programs and optimization algorithms has attracted considerable attention in increasing the speed and accuracy of material design and saving much cost. In this paper, a method for predicting the thermal conductivity of asbestos frictional composites with Abaqus-Python is proposed and the optimum composition is determined using PSO algorithm. We have manufactured composite specimens of the corresponding composition and tested for friction coefficient, wear rate and thermal conductivity measurements, and the simulated result goes will with experimental result.