Modeling and prediction of metallic powder behavior in explosive compaction process by using genetic programming method based on dimensionless numbers

Abstract

Explosive compaction process of metallic powders has been studied using a semi-empirical method. This method utilizes dimensional analysis along with genetic programming approach to obtain an expression relating the final density of compacts to the effective parameters during compaction process such as shock compaction energy, properties of metallic powder, and geometry of the problem and explosive charge. Dimensionless numbers have been constructed based on the effective parameters using a complete set of input–output experimental data. The obtained dimensionless numbers then have been applied as input–output data pairs for genetic programming optimization process considering modeling error as the objective. The obtained results show that the proposed model using dimensional analysis method along with genetic programming can predict the final density of compacts with 99.8% accuracy. Also, the outputs of the proposed model have been compared with those obtained by group method of data handling type neural network in the literature. Consequently, genetic programming method has much less root mean square error than group method of data handling model and can be successfully used for modeling and prediction of the complex process behavior.