Estimation of Friction Coefficients of Soybean Seeds with Soft Computing Approach

Abstract

Determination of physical and mechanical properties of agricultural products plays an important role in the usage areas of the products and industrial applications. Correct determination and evaluation of physical and mechanical properties of agricultural products is of critical importance in determining the quality, durability and usage potential of the product. In this study, the relationship between moisture content and friction coefficients of Samsoy variety soybean seed, which is a trial material, was determined in order to contribute to making correct decisions in industrial design and material selection. The central aim of this research is to expose with different moisture contents and friction surfaces well-accepted data-driven models to predict friction coefficients for soybean seed using different soft computing techniques. Determination of friction coefficient of agricultural products is important in terms of design and functionality of equipment used in post-harvest technologies and agricultural applications. In the study, 3 different moisture contents and five different friction surfaces (steel, stainless steel, galvanized sheet, PVC, court fabric) were used. Artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), group method of data handling (GMDH) are used to predict of friction coefficients. The best accuracy values were recorded as GMDH 7-7-1 for seven input and 7-15-1 model for five input structures for kinetic and static friction that were calculated performance criteria R2 = 0.99-0.98, RMSE =0.00004-0.00006 , MSE = 0.00009 -0.00011, respectively. These selected the best models predicted which can be used in the soft computing techniques determined different conditions to estimating the friction coefficient for soybean seeds.