In this study, the fatigue damage to a power takeoff (PTO) shaft was evaluated under various operating conditions in rotary-tillage operations, considering soil strength and texture. Pearson correlation analysis was conducted to identify the significant variables influencing PTO shaft fatigue damage, and a prediction formula was derived through regression analysis using these variables. The PTO shaft exhibited increased shear stress with higher transmission gear stages, PTO gear stages, or soil properties, including strength and texture. The fatigue damage increased with higher transmission gear stages and soil strength while decreasing with higher PTO gear stages. Notably, as the PTO gear stage increased, the mean stress increased; however, the stress amplitude and equivalent completely reversed stress significantly reduced fatigue damage. Statistical analyses revealed a strong correlation between PTO shaft fatigue damage and factors such as tractor travel speed, PTO shaft power consumption, PTO shaft rotational speed properties, including strength and texture. The developed prediction equation, incorporating all significant variables, demonstrated, with a coefficient of determination (R2) of 0.93 and a root mean square error (RMSE) of 2.94x10-9. This equation effectively identifies trends in PTO shaft fatigue damage based on key operational variables. Furthermore, the findings emphasize the critical role of soil texture in assessing PTO shaft fatigue damage.

Mechanical tillage before cotton sowing is a crucial process in cotton production. Numerical simulations of soil cutting and energy consumption predictions, along with optimization methods, are very important for understanding the interaction between tillage tools and soil, as well as guiding energy-efficient cultivation practices. The focus of this study is on the problem of cutting sandy silt in Xinjiang cotton fields. Sandy silt can be characterized by its low cohesion and large, loose particles. Starting from the macroscopic physical and mechanical properties of the soil, a soil contact mechanics model considering soil plastic deformation and bonding forces between soil particles is established. By optimizing the cotton field soil discrete element model and parameter calibration methods, the accuracy of the soil cutting simulation is improved. The principles and modelling steps of discrete element method (DEM) simulations for cutting soil are explained in detail, enabling the analysis and evaluation of the complex dynamic behaviour of soil under large deformation conditions and the mechanical properties of the cutting tool. The average error between the energy consumption measured in field rotary tillage experiments and simulation results is 7.04%. By utilizing the simulation results as a dataset, an extreme learning machine (ELM) without a physical model is employed to replace traditional polynomial regression for rapid energy consumption prediction based on the cutting parameters. The average error between the prediction results and simulation results is 4.34%. By using response surface methodology based on the predicted energy consumption, optimal working parameters are determined, resulting in a 10.02% reduction in the power consumption compared to the initial parameter settings. This effectively achieves energy savings in rotary tillage and further validates the accuracy of the simulation method and prediction model.