An analysis of the influence of hand hole and ventilation hole design on compressive strength of corrugated fiberboard boxes by an artificial neural network model

This research aimed at developing a high-performing corrugated fiberboard box compression strength prediction model and to analyze the influences of ventilation and hand hole designs for these containers on the box compression test (BCT) by applying artificial neural network (ANN) modeling. The input variables considered in this study are composed of nine parameters including box dimension as well as shapes, sizes, positions, and locations of ventilations and hand holes of a regular slotted container (RSC, FEFCO 0201). Back propagation algorithms (BPNs) of ANN models were developed from 970 BCT testing data points (single wall boards, C flute, 205/112/205 g/m²). Tested data was randomly broken into three groups for the model development as 80:10:10 for the training set, testing set, and validating set. According to the analysis performed, a BPN 9-13-1 model reflected the highest prediction performance with R² = 0.97. According to the analysis, BCT was significantly affected by the hand hole location followed by the geometrical dimensions of the box (height, length, and width) and the ventilation factors (shape, number, and location) in that order. Hand holes at the top flaps caused a lower BCT reduction compared with those at the vertical locations of the box. Slight changes to the eliminated board area for both hand holes and ventilation (±5%) contributed to less BCT reduction compared with its locations and shapes. Interestingly, increasing the box height significantly increased the BCT, and this was found to be limited only to shorter boxes fabricated from a high stiffness corrugated board.