Modeling and Predicting the Forces on Moldboard Plough by using Response Surface and Artificial Neural Network

Abstract

Introduction Tillage is a very important operation that influences the growth and productivity of agricultural products. It is necessary to introduce some conditions to improve soil physical properties, aeration, permeability and root development in tillage operations. However, in primary tillage, especially when moldboard ploughs are used, this may be time consuming and costly for researchers to use it in their research. Some researchers use physical experiments to perform the work, which the accuracy of the results is dependent on the measuring instruments precision. However, some other researchers use simulation and mathematical modeling to reduce the time and costs and increase the relative accuracy of the research results. Many studies have also shown that modeling the forces involved in tillage is a good way to estimate the performance of different tillage tools and improve their geometry. However, the key to success in numerical simulation of tillage operations is to simulate the exact instrumentation, based on the correct assumptions as well as the proper methods. The prediction of the forces involved in tillage tools has an important role in their design. Collecting data on the forces involved in tillage tool under different farm conditions is a time consuming and costly task. Therefore, the prediction of a tillage tool forces is very important for the designer and the user in order to achieve better performance of the tool. Materials and Methods In this study, a cylindrical moldboard made by Alpler Company in Turkey was used to simulate the moldboard. A measuring device was designed and constructed to measure the various points of the desired moldboard. Then, the spatial points obtained by the measuring device were presented to the SolidWorks 2016 software and the desired moldboard was modeled. The finite element method by Abacus 2016 was then used to simulate the interaction between soil and moldboard. Treatments used in simulated tillage operations included tillage depths (5, 10, 15, 20 and 25 cm) and forward speed (1, 1.5, 2, 2.5 and 3 millimeters per second). The independent variables were considered as tensile, vertical and lateral forces (Kilo newton). After simulating the tillage operations, tensile, vertical and lateral forces were obtained. These forces were modeled using response surface and artificial neural networks techniques. Then, the obtained models were compared using R2, RMSE and MRDM statistical indices and the best model was selected. Results and Discussion When using the response surface method, the quadratic model was selected by using the maximum value of the statistical indices R2, R2a and R2p, among the linear, two-factor and quadratic models. Then, the significance of model variables was evaluated by using variance analysis. The forces were also modeled by using the neural network method. According to the fitting curves and statistical indices of R2, RMSE and MRDM for the tensile, vertical and lateral forces, it is revealed that both methods could well predict the forces but artificial neural network was more suitable than the response surface method. Moreover, by investigating the interactions of tillage treatments and forward speed on the forces in this research, it was observed that by increasing the depth of tillage and velocity, tensile, vertical and lateral forces were increased nonlinearly by 66.55%, 68.47%, and 64.76%, respectively. Conclusion Regarding all the results obtained from this study, it can be concluded that the developed models using the artificial neural network in this research was a good and powerful tool for predicting the forces involved in moldboard ploughs both in the field operations and in related studies. It is also recommended that the developed models in this study can be used to manage the tillage operations, such as selecting the proper tractor. However, it is also suggested that other affecting factors, such as moldboard angles, should be included in future models to increase the ability of the model to predict the forces involved in moldboard plows.

Keywords

• artificial neural network
• finite element modeling
• mouldboard plough
• response surface methodology
• simulation