Two soft-computing techniques are implemented to model and optimize the compressive strength of carbon/polymer composites. Artificial neural network is used to establish a relationship between the uniaxial compressive strength of fabricated materials and the most significant processing parameters. To put together a database, three different types of wood are carbonized at various heat treatment temperatures, in specific pyrolysis time periods. Compression tests are then conducted at room temperature on the composites, at a constant strain rate. The collected data of compressive strength and the related fabrication parameters are used as sets of data for training a neural network. A nested cross validation scheme is used to ensure the efficiency of the network. Results are indicative of a very good network, which generalizes very well. Next, an attempt is made to optimize the compressive behavior of the composites by controlling carbonization temperature, time and also starting material type with the aid of a genetic algorithm coupled with the trained network. The optimization system yields promising results, significantly enhancing the compressive strength. The validity of the optimal experiment, as proposed by the soft-computing system, is verified by subsequent laboratory testing. 
Intelligent use of data to optimize compressive strength of cellulose-derived composites