Estimation of wavy honeycombs' compression performance via a machine learning algorithm

Abstract

In this study, the wavy honeycomb's initial peak crushing force (IPCF) and energy absorption (EA) were estimated using the decision tree algorithm. First, using experimental results, Ls-Dyna models of honeycombs were verified. In this way, the stress-strain curves and shapes were compatible. Secondly, the effect of parameters was examined. Waves contribute significantly to values. In particular, for honeycombs with the same geometric properties, when the wavenumber is 3, the IPCF and specific energy absorption (SEA) values increase by 121.59% and 75.08%, respectively. In addition, when the wave amplitude is 0.15mm, IPCF and SEA increase by 60.89% and 71.3%, respectively. Afterward, using the full factorial, a data set with various parameter values was prepared. The parameters (inputs) and values (outputs) in the data set were used to train and verify the decision tree algorithm using Python. Finally, new data was introduced into the algorithm, and values were estimated. Errors ranged from 0.17% to 14.65% between Ls-Dyna and the algorithm results. These findings show that machine learning is suitable for wavy honeycombs.