Modeling and Optimization of Aloe-Vera Static Shearing by Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Methods

Knowledge of plants' mechanical properties and behavior is one of the essential factors in the design of harvesting and post-harvesting devices. In this study, horticulture, medicinal, and food plants of Aloe Vera were cut using a flat blade. Aloe Vera leaves are cut from the cross-section, and the force and energy required for cutting them were measured using Zwick /roll universal testing machine. The effect of cutting angle (0, 30, and 45 degrees), cutting speed (150, 250, 350, and 450 mm/min), and thickness of Aloe Vera leaves (1, 2, and 3 cm) on the force and energy required for cutting were investigated. To achieve this, response surface methodology was used, the results of which were compared with the artificial neural network method. The results of this study indicated that by increasing the cutting angle, cutting rate (cutting speed), and thickness of the leaves of the Aloe Vera plant, the energy required for its cutting decreased. The most optimal case for cutting the Aloe Vera plant in the case of cutting angle is 45 degrees, the cutting speed is 450 mm/min, and the thickness of Aloe Vera leaves is 3 cm, in which the required energy for cutting and cutting force is equal to 3.45 J and 4.99 N respectively. This study showed that Response Surface Methodology (RSM) is suitable for evaluating optimum conditions in Aloe Vera cutting experiments and is more accurate than the Artificial Neural Network (ANN) method.