Prediction of Draft Force and Disturbed Soil Area of a Chisel Tine in Soil Bin Conditions Using Draft Force and Its Comparison with Regression Model

Çarman, Kazım; Marakoğlu, Tamer; Taner, Alper; Çıtıl, Ergün

doi:10.15316/sjafs.2020.229

Cited by 6 publications

(6 citation statements)

References 9 publications

(7 reference statements)

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“…The measurement of the soil tillage draft forces generated by the action of the working bodies in the soil in the agricultural machines intended for soil processing is a widely discussed problem in the literature. From the measurement with the dynamometer to complex measurements with force sensors or tensometric methods (simple or using complex tensometric frames), the authors tried to estimate the soil tillage draft forces during its processing, (Askari M., et al, 2013;Carman K., et al, 2021;Rashidi M., et al, 2013;Afify M.T., et al, 2020;Abbaspour_Gilandeh Y., et al, 2020), (Abbaspour-Gillandeh M., et al, 2020;Markinos A., et al, Croitoru St., et al, 2016;Vlăduţ D.I., et al, 2017). The resistance of the soil to deformation has a direct impact on energy usage and the effectiveness of agricultural work.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Random Intensity of the Soil Tillage Draft Forces in the Supports of the Working Bodies of a Cultivator

CARDEI,

CONSTANTIN,

MURARU

et al. 2023

INMATEH

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The investigation on the unpredictability of the distribution of traction resistance forces on the working bodies of a specific type of MCLS complex cultivator is presented in the paper. The validation of the random character of the force that loads the active bodies is used to indicate the mathematical model that must be followed for the research of the soil processing system. Also, the research results elucidate some hypotheses issued in the conception and design of the machine: the more intense load for the working bodies from the lines of working bodies located immediately after the tractor, the existence of working bodies that are constantly more intensively requested and the causes. According to the literature, it is to be assumed that the forces that demand the working bodies, the supports, and the machine frame have a random nature. The conclusions of the descriptive, inferential statistical study (which do not quantify the random intensity) are explained together with the results. The experimental loads are compared to some of the most random strings to produce a quantitative estimate of the random intensity. As a result, processes that use random functions and the entirety of their approach are recommended in mathematical modelling for further research

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Section: Introductionmentioning

confidence: 99%

Estimation of the Random Intensity of the Soil Tillage Draft Forces in the Supports of the Working Bodies of a Cultivator

CARDEI,

CONSTANTIN,

MURARU

et al. 2023

INMATEH

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“…For this purpose, speed and working depth are used as predictors, as are others (Afify et al,2020;Kim et al, 2020;Rashidi, 2013a and2013b;Saleh et al, 2021). Artificial neural networks have started to be used in the last decades to obtain predictions of the parameters targeted in the field of agricultural soil processing Çarman et al, 2021;El Wahed, Aboukarima, 2007;Al-Dosary et al, 2020;Carman et al, 2019;Askari, Abbaspour-Gilandeh, 2020). Also, nonlinear regression formulas for the problem of predicting the draft tillage force are used in and Almaliki et al (2019), in the case of a disc plough.…”

Section: Introductionmentioning

confidence: 99%

Superior Inferential Statistics of the Experimental Data of a Complex Experimental Cultivator

CARDEI,

CONSTANTIN,

PERSU

et al. 2023

INMATEH

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The aim of the research is to highlight some statistical tools that favour extracting the components of the dynamic process that are dependent on the forward speed of some agricultural aggregates. The main objectives are: (I) identification of a minimum number of components in a multitude of random variables, with the help of which the other random variables can be calculated, and the application of this result to the strain gauge measurements; (II) establishing the connection between the synthetic results that partially solve the first objective and the forward speed of the agricultural aggregate. The second objective is used to obtain indications in search of the parameters’ dependencies on the forward speed of the aggregate. The first objective seeks to determine a group of three signals from the twelve, with the help of which the best multivariate linear interpolation is obtained for the other nine signals, which in physical terms means the reduction to a quarter of the measurement points and of the strain sensors used. A result associated with the first objective refers to the estimation of information loss due to the limited number of deformation sensors mounted on the tested structure. The article also presents attempts to use the results of the theory of neural networks and statistical interaction. In order to capitalise on the experimental data in this complex statistical framework, it is necessary to monitor at least the working speed (not only the average speed per experiment), fuel consumption, working depth (continuously monitored), soil moisture etc.

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“…The results displayed that in comparison to the ANN model, the MLR model had both worse accuracy and a lower correlation coefficient. Çarman et al [25] employed MLR and ANN approaches to forecast the horizontal force and the area of plowed soil by operation of a chisel tine. The study inputs were blade (share) width, tillage speed, and working depth.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Artificial Neural Network to Model Performance Attributes of a Mechanization Unit (Tractor-Chisel Plow) under Different Working Variables

2022

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The focal objective of the current research is to apply artificial neural network (ANN) and multiple linear regression (MLR) methods for modeling the performance attributes of a mechanization unit (tractor-chisel plow) during the plowing process under both different soil types and working variables. Two different parameters to represent working conditions and soil type were considered as potential input parameters. The first parameter represented soil type by calculating soil texture index as a combination of clay, silt, and sand. The second one was constructed into one dimensionless parameter, namely the field working index. This index linked most working variables such as plowing speed, plow width, soil moisture content, soil bulk density, tractor power, and plowing depth. The performance of the created ANN and MLR models was appraised by computing mean-absolute-error criterion for the testing dataset. The mean absolute error values for draft force, effective field capacity, fuel consumption, drawbar power, overall energy efficiency, rate of plowed soil volume, and loading factor, were 1.44 kN, 0.03 ha/h, 1.17 L/h, 2.28 kW, 0.68%, 73.97 m3/h, and 0.08 (decimal), respectively, when the ANN model was applied. In addition, coefficient of determination (R2) acted as a criterion for judging the performance of the developed models, and their values when ANN was applied were 0.569, 0.384, 0.516, 0.454, 0.486, 0.777, and 0.730 for the same performance attributes, respectively. When the MLR model was applied, the corresponding values of R2 were 0.239, 0.358, 0.352, 0.429, 0.511, 0.482, and 0.422, respectively, for the same attributes. The current study adds to the standing literature by contributing data and information regarding the performance attributes of a tractor-chisel plow unit under specific working variables and soil types. In addition, the models developed for plowing operations in different soil texture and under the field working index are recommended for use in budgeting for diesel consumption, in calculating operation cost, in matching mechanization units of tractor-chisel plows, in estimating energy requirements of tractor-chisel plow combinations, etc.

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Prediction of Draft Force and Disturbed Soil Area of a Chisel Tine in Soil Bin Conditions Using Draft Force and Its Comparison with Regression Model

Cited by 6 publications

References 9 publications

Estimation of the Random Intensity of the Soil Tillage Draft Forces in the Supports of the Working Bodies of a Cultivator

Estimation of the Random Intensity of the Soil Tillage Draft Forces in the Supports of the Working Bodies of a Cultivator

Superior Inferential Statistics of the Experimental Data of a Complex Experimental Cultivator

Evaluation of Artificial Neural Network to Model Performance Attributes of a Mechanization Unit (Tractor-Chisel Plow) under Different Working Variables

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