Evaluation of delamination damages on composite plates using techniques of image processing and analysis and a backpropagation artificial neural network

Abstract

Nowadays, drilling of carbon/epoxy laminates is extremely frequent in manufacturing and assembling processes and is normally carried through using standard drills, like twist or Brad drills. However, it is always necessary to have in mind the need to adapt properly the drilling operations and/or the drilling tools used as the risk of delamination occurrence in the laminates involved, or other kind of damages, is very high. Moreover, delamination can be critical because the mechanical properties of the produced parts can be severely affected. Thus, the production of higher quality holes, with damage minimization, is a key challenge to everyone related with composites industry to develop adequate methodologies to delamination characterization and assessment. In this paper, the delamination caused on laminates by drilling machining operations is analytically evaluated by processing and analyzing enhanced conventional radiography images of the laminates involved. In resume, in order to evaluate the delamination damage in laminates plates caused by drilling operations, the radiography images acquired are processed using a computational methodology that uses techniques of image processing and analysis and a backpropagation artificial neural network. Experimental results show that the proposed methodology can be successfully used to measure and characterize the delaminated area. Hence, using our methodology, the damage evaluation on laminates can become more accurate, efficient and simple.