&lt;b&gt;Simtrac – an application for simulation of traction efficiency of agricultural tractors with front wheel assist

Santos, Fábio Lúcio; Queiroz, Daniel Marçal de

doi:10.4025/actascitechnol.v38i4.29218

Cited by 4 publications

(2 citation statements)

References 29 publications

(43 reference statements)

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“…Tillage is important in seedbed preparation, seed placement, the addition of nutrients and other items to the soil, and in the management of water and pest levels (Lobb et al, 2007). Selecting the ideal chisel plow can be difficult, as multiple operational variables and parameters must be considered (Santos & de Queiroz, 2016). The quality of the chiseling process depended on the depth of cut and working speed (Ibrahim et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Predicting the Performance Parameters of Chisel Plow Using Neural Network Model

2020

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This study examines the capability of an artificial neural network (ANN) approach using a backpropagation-learning algorithm to predict performance parameters for a chisel plow at three field sites with differing soils. The draft force, effective field capacity (EFC), fuel consumption rate (FC), overall energy efficiency (OEE), and rate of plowed soil volume (SVR) were predicted at varying plowing speeds, plowing depths, soil moisture contents, soil bulk densities, soil texture indexes, and tractor powers. Collected field data was divided into a training set (for predicting the required parameters) and testing set (for model validation). For the ANN algorithm, the number of hidden layers, neurons, and transfer functions were varied to construct different ANN architectures, which were then verified using various statistical criteria, such as mean absolute error. The results showed that an ANN with one hidden layer and 15 neurons was ideal. The developed ANN model predicted the draft force, EFC, FC, OEE, and SVR of the chisel plow with a mean absolute error of 3.23 kN, 0.80 hah-1 , 3.04 Lh-1 , 2.78% and 1.06 m 3 h −1 , respectively in the testing phase.

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Section: Introductionmentioning

confidence: 99%

Predicting the Performance Parameters of Chisel Plow Using Neural Network Model

2020

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“…One way to obtain the torque and power values of a tractor's engine is through the use of a dynamometer [30]. Also, some software solutions have been developed for these and other types of calculations [31], and mobile applications, which are a complement to the study of tractors [32].…”

Section: Introductionmentioning

confidence: 99%

Solving Power Balance Problems in Single-Traction Tractors Using PTractor Plus 1.1, a Possible Learning Aid for Students of Agricultural Engineering

et al. 2018

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Tractors are used to perform jobs that require different types of agricultural tools to be attached to their rear, to their front, or both. These tools may need to be dragged, towed, or suspended above ground, and sometimes require a power supply; this is usually obtained via a hydraulic system or from the tractor's power takeoff system. When tractors have to work with such tools on different types of soils and on different slopes, the need arises to calculate the power the tractor engine will have to produce. In the classroom, this is normally calculated manually with the help of a calculator. This work, however, describes a computer program (written in Delphi and operating under Windows) that rapidly solves the most common types of power balance problems associated with single-traction tractors. The value of this software as a learning aid for students of agricultural engineering is discussed.

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