Knowledge based data boosting exposition on CNT-engineered carbon composites for machine learning
Sunil C. Joshi
Abstract
Machine learning (ML) is useful in predictive analytic or prognostic modeling for materials and engineering. It is, however, challenging to gather sufficient and representative data. Experiments are possible only in small numbers due to specialty materials, manufacturing, infrastructure, and testing involved. Simulation and numerical models need skills and appropriate validation. If the dataset at hand is too small in size to train ML, professionals tend to create synthetic data, which may not necessarily meet the quality required of the new data. A Knowledge-based Data Boosting (KDB) process, named COMPOSITES, that rationally addresses data sparsity without losing data quality is systematically discussed in this paper. A study on inter-ply fracture toughness of carbon nanotube (CNT)-engineered carbon fiber reinforced polymer (CFRP) composite laminates is used to demonstrate the KDB process. This involved strengthening of inter-ply interfaces using CNT advocated for improving delamination resistance of the CFRP composites. It is demonstrated that the KDB process helped augment the dataset reliably and improved the best fit regression lines. The process also made it possible to define boundaries and limitations of the augmented dataset. Such sanitized dataset is certainly valuable for prognostic modeling.