EVALUATION OF USING A COMBINATION OF SIMULATED AND EXPERIMENTAL DATA TO PREDICT DRAFT FORCE OF A MOLDBOARDPLOW | ||||
| Misr Journal of Agricultural Engineering | ||||
| Article 1, Volume 30, Issue 1, January 2013, Page 1-28 PDF (1.26 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjae.2013.102082 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Abdulwahed M. Aboukarima1, 2 | ||||
| 1Researcher, Agricultural Engineering Research Institute, Agriculture Research Centre | ||||
| 2Assistant Professor at Shaqra University, Saudi Arabia. | ||||
| Abstract | ||||
| Information is required on draft force for tillage implements as it plays an important role in design and development of such implements.Due to the complexity of draft force prediction models of the moldboard plow, there is a need to develop a simple draft prediction model of the moldboard plow, as affected by soil properties and working conditions. In this research, two models were implemented. The first one was by artificial neural network (ANN) and the second was by a multiple linear regression (MLR). The required draft data were obtained by the available Excel spreadsheet. The soil parameters required in the spreadsheet were obtained from experimental work at different sitesin Saudi Arabia. For generating draft data, the plowing depths and the plowing speeds were assumed. All combinations were addressed and the total data were 2268 rows. However, 2172 rows were used to build the ANN and MLR models for predicting draft of a moldboard plow. Meanwhile, 96 data points were used to test the models. The mean relative error (MRE) between simulated and predicted values, using regression draft equation and ANN model were 1.86% and -8.966%, respectively during testing phase. The performance of the two models was validated by a field experiment data and points from literature. MRE values between measured and predicted values of validation data using field data were 5.19% and 12.32% when using ANN and MLR models, respectively. The encouraging results can push to utilize the developed models to be a tool for evaluation in farm machinery management process. | ||||
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