A comprehensive investigation on static and dynamic friction coefficients of wheat grain with the adoption of statistical analysis

摘要

class="kwd-title">Abbreviations: 2DC, two-dimensional chart; 3DC, three-dimensional chart; ANOVA, analysis of variance; DFC, dynamic friction coefficient; DIE, dual interaction effect; DMRT, Duncan’s multiple range test; GMD, geometric mean diameter; MRDM, mean relative deviation modulus; MLR, multiple linear regression; RMSE, root mean square error; SFC, static friction coefficient; SE, single effect; TIE, triple interaction effect; MAVET, mean of absolute values of error term class="kwd-title">Keywords: Analysis of variance, Duncan’s multiple range test, Moisture content, Sliding velocity, Contact surface class="head no_bottom_margin" id="ab010title">AbstractThis paper deals with studying and modeling static friction coefficient (SFC) and dynamic friction coefficient (DFC) of wheat grain as affected by several treatments. Significance of single effect (SE) and dual interaction effect (DIE) of treatments (moisture content and contact surface) on SFC and, SE, DIE, and triple interaction effect (TIE) of treatments (moisture content, contact surface and sliding velocity) on DFC were determined using statistical analysis methods. Multiple linear regression (MLR) modeling was employed to predict SFC and DFC on different contact surfaces. Predictive ability of developed MLR models was evaluated using some statistical parameters (coefficient of determination (R²), root mean square error (RMSE), and mean relative deviation modulus (MRDM)). Results indicated that significant increasing DIE of treatments on SFC was 3.2 and 3 times greater than significant increasing SE of moisture content and contact surface, respectively. In case of DFC, the significant increasing TIE of treatments was 8.8, 3.7, and 8.9 times greater than SE of moisture content, contact surface, and sliding velocity, respectively. It was also found that the SE of contact surface on SFC was 1.1 times greater than that of moisture content and the SE of contact surface on DFC was 2.4 times greater than that of moisture content or sliding velocity. According to the reasonable average of statistical parameters (R² = 0.955, RMSE = 0.01788 and MRDM = 3.152%), the SFC and DFC could be successfully predicted by suggested MLR models. Practically, it is recommended to apply the models for direct prediction of SFC and DFC, respective to each contact surface, based on moisture content and sliding velocity.