Performance of combined offset disc harrow (front active and rear passive set configuration) in soil bin

Lee

et al. 2021

Sensors

The objective of this study was to develop a model to estimate the axle torque (AT) of a tractor using an artificial neural network (ANN) based on a relatively low-cost sensor. ANN has proven to be useful in the case of nonlinear analysis, and it can be applied to consider nonlinear variables such as soil characteristics, unlike studies that only consider tractor major parameters, thus model performance and its implementation can be extended to a wider range. In this study, ANN-based models were compared with multiple linear regression (MLR)-based models for performance verification. The main input data were tractor engine parameters, major tractor parameters, and soil physical properties. Data of soil physical properties (i.e., soil moisture content and cone index) and major tractor parameters (i.e., engine torque, engine speed, specific fuel consumption, travel speed, tillage depth, and slip ratio) were collected during a tractor field experiment in four Korean paddy fields. The collected soil physical properties and major tractor parameter data were used to estimate the AT of the tractor by the MLR- and ANN-based models: 250 data points were used for developing and training the model were used, the 50 remaining data points were used to test the model estimation. The AT estimated with the developed MLR- and ANN-based models showed agreement with actual measured AT, with the R2 value ranging from 0.825 to 0.851 and from 0.857 to 0.904, respectively. These results suggest that the developed models are reliable in estimating tractor AT, while the ANN-based model showed better performance than the MLR-based model. This study can provide useful results as a simple method using ANNs based on relatively inexpensive sensors that can replace the existing complex tractor AT measurement method is emphasized.

Section: Introductionmentioning

confidence: 99%

Estimation of Axle Torque for an Agricultural Tractor Using an Artificial Neural Network

Lee

et al. 2021

Sensors

“…However, in these studies, because tillage depth could not be accurately measured, only the effect of random tillage depth was analyzed, and the approximate measurement of tillage depth was made by manual measurement. In addition, since it was not possible to measure the tillage depth in real time during tillage operation, some studies have been conducted in an indoor soil bin [20,21]; however, in this environment it is difficult to consider the soil properties of hardpan, one of the representative properties of soil where tillage operations are performed. Repeated use of large farm machinery, such as agricultural tractors, results in the formation of hard plates at the bottom of the plow layer, by compression of the soil surface [22].…”

Section: Introductionmentioning

confidence: 99%

Power Transmission Efficiency Analysis of 42 kW Power Agricultural Tractor According to Tillage Depth during Moldboard Plowing

Siddique

et al. 2020

Agronomy

In order to optimize tractor design and optimize efficiency during tillage operation, it is essential to verify the impact through field tests on factors affecting the tractor load. The objectives of this study were to investigate the effect of tillage depth on power transmission efficiency of 42 kW power agricultural tractor during moldboard plowing. A load measurement system and a tillage depth measurement system were configured for field tests. To analyze the effect of tillage depth on power transmission efficiency and fuel consumption, the data measured in the three-repeated field test were classified according to tillage depth. As the tillage depth increased from 11 cm at the top of the hardpan to 23 cm at the deepest, the required power of the engine increased by approximately 13% from 35.48 kW to 40.11 kW, and the power transmission efficiency also increased significantly from 66% to 95%. Among them, the power transmission efficiency of the rear axle was significantly increased from 38% to 59%, which was the most affected. As the tillage depth increased, the overall power requirement is greatly increased due to the resulting workload, but the fuel consumption and the specific fuel consumption are reduced because the engine speed of the tractor is reduced. As the tillage depth increased from 11 cm to 23 cm, the fuel consumption rate was rather reduced by 13.5% as the engine rotational speed decreased 11.3% due to the increase work load of tractor. In addition, the specific fuel consumption decreased from 302.44 g/kWh to 236.93 g/kWh, showing a fuel consumption saving of up to 21.7% during moldboard plow. In addition, as the tillage depth increased, the ratio of the value excluding the mechanical and hydraulic power requirements has significantly decreased from 34% to 5% as the power transmission efficiency increases. This study considers the soil properties according to the soil depth, as well as the power transmission efficiency and fuel consumption rate. The research results can provide useful information for research on power transmission efficiency and selection of an appropriate power source of agricultural tractor according to tillage depth during moldboard plowing and are expected to be used in various ways as basic studies of digital farming research in agricultural machinery.

“…Generally, the axle torque of a tractor differs according to the various conditions such as the soil environment, gear stage, and working type [1,2]. Amongst previous studies, there are some studies on axle torque measurement according to the attached implement [3,4], working speed [5][6][7], tillage depth [6,8], and soil conditions [9][10][11]. However, in order to measure axle torque, a telemetry system that allows for the twisting of a line is required, because the axle rotates, which is very expensive.…”

Section: Introductionmentioning

confidence: 99%

Development of a Prediction Model for Tractor Axle Torque during Tillage Operation

Baek

et al. 2020

Applied Sciences

In general, the tractor axle torque is used as an indicator for making various decisions when engineers perform transmission fatigue life analysis, optimal design, and accelerated life testing. Since the existing axle torque measurement method requires an expensive torque sensor, an alternative method is required. Therefore, the aim of this study is to develop a prediction model for the tractor axle torque during tillage operation that can replace expensive axle torque sensors. A prediction model was proposed through regression analysis using key variables affecting the tractor axle torque. The engine torque, engine speed, tillage depth, slip ratio, and travel speed were selected as explanatory variables. In order to collect explanatory and dependent variable data, a load measurement system was developed, and a field experiment was performed on moldboard plow tillage using a tractor with a load measurement system. A total of eight axle torque prediction regression models were proposed using the measured calibration dataset. The adjusted coefficient of determination (R2) of the proposed regression model showed a range of 0.271 to 0.925. Among them, the prediction model E showed an adjusted R2 of 0.925. All of the prediction models were verified using a validation set. All of the axle torque prediction models showed an mean absolute percentage error (MAPE) of less than 2.8%. In particular, Model E, adopting engine torque, engine speed, and travel speed as variables, and Model H, adopting engine torque, tillage depth and travel speed as variables, showed MAPEs of 1.19 and 1.30%, respectively. Therefore, it was found that the proposed prediction models are applicable to actual axle torque prediction.