Comparative analysis of fractal dimension and novel geometric indices for assessing branch cutting quality

Abstract

The limitations of the fractal dimension analysis are addressed in this study through the introduction of innovative geometric parameters: Surface Smoothness Index (SSI), Area Deformation Coefficient (ADC), and Perimeter Deformation Coefficient (PDC). The effects of blade curvature, cutting speed, branch diameter, and moisture content on these indices were studied using a systematic approach. Image analysis was performed using ImageJ software to ensure accurate measurements in the evaluation of sheared Morus alba branches. Using Response Surface Methodology (RSM), the effects of various input parameters on cutting quality were examined. A single quality parameter, here fractal dimension (FD), was found to be insufficient for capturing the complexities of cutting quality, as none of the main, nonlinear, or interaction effects emerged as significant in the full quadratic model. This is while the novel geometric indices introduced here were able to effectively capture the intricate relationships in cutting quality. The results indicated that optimizing cutting parameters, namely, cutting speeds above 400 mm/min and blade curvature radii greater than 47 mm significantly improved cutting quality. It was determined that maintaining branch moisture content in the 40 to 42 % range is optimal for achieving acceptable performance. Overall, the study emphasized the importance of adjusting these input variables to enhance cutting effectiveness across various branch diameters. By taking the combined geometric parameters into account, more informed decisions could be made in pruning practices.