A New Multiparameter Approach to the Prediction of Wear Rates in Agricultural Sprayer Nozzles

A probabilistic multiparameter approach is presented in this paper for the prediction of the combined effects of multiple variables on wear rates in the material of agricultural sprayer nozzles. The methodology developed is based on the principles of stochastic processes in mathematical statistics, which essentially involve the statistical formulation of the mathematical models of wear rates as functions of the multiple random variables that may affect agricultural sprayer nozzle wear rates, and the determination of the constants of the probabilistic model using multivariate regression analysis. A general empirical approach is proposed for the estimation of nozzle wear rate. The predictive capacity of the empirical approach is then verified by comparing the relevant theoretical model predictions with experimental data. When this exercise was carried out, excellent correlations were obtained between theoretical model predictions and experimental data. An important feature of the model is that assessments can be made for the relative contribution of the effect of each random variable on the total wear rate at any point in time. The importance of the new multiparameter models and the interdisciplinary collaboration of experts in the fields of structural materials, mechanics, and probabilistic methods in order to control and minimize wear rates in the design of nozzles for food production and agricultural engineering applications are also discussed.