Grain flow measurements with X-ray techniques

Arslan, Selçuk; Inanc, Feyzi; Gray, Joseph N.; Colvin, T. S.

doi:10.1016/s0168-1699(00)00074-0

Cited by 23 publications

(12 citation statements)

References 3 publications

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“…Furthermore, the percentage of the opening area of the auger available to the paddle of interest during these times is equivalent. It follows that 4) which simplifies to .5) Noting that the elapsed time between t 3 and t 1 is equivalent to d V , the mass deposited onto a single paddle for any arbitrary cross-sectional area opening of the auger is described by .6) Appendix C Paddle filling process during circular motion for an arbitrary cross sectional auger opening…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the percentage of the opening area of the auger available to the paddle of interest during these times is equivalent. It follows that 4) which simplifies to…”

Section: Discussionmentioning

confidence: 99%

“…Indirect methods encompass a capacitive sensor [7], an ultrasonic sensor [7], and x-ray measurement principles [4].…”

Section: Table Of Contents Introductionmentioning

confidence: 99%

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Self-Calibrating Mass Flow Sensor

Reinke

Dankowicz

2012

Volume 6: 1st Biennial International Conference on Dynamics for Design; 14th International Conference on Advanced Vehicle Techn

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A comprehensive analysis was conducted for increased accuracy and self-calibration for a mass flow sensing system on a combine. This was undertaken as part of the John Deere Technology Innovation Center (JDTIC)-sponsored research program "Self-calibrating mass-flow sensor", in turn part of a John Deere Moline Technology Innovation Center (MTIC) effort toward optimization and closer integration of the components of the mass-flow sensing system in Deere harvesting combines. The long-term objective was to achieve a self-calibrating sensor system capable of adapting to varying input conditions due, for example, to changes in grain moisture content and aging of the system's elevator paddles.In analyzing the mass flow sensing system, a physics-based model was developed to describe the relationship between the rate of mass flow through the combine and the measured force imparted to the impact plate in terms of mechanical properties of the grains and the interior geometry of the combine. A computational realization of this model was constructed in Matlab. Accurate mass flow rate estimation was achieved through model-based estimation based on nonlinear regression applied to the physics-based model and data acquired through simulation and experimentation. Model-based estimation was also extended as a means for self-calibration of the sensing system. Through development of the physicsbased model, the dependence of the force imparted to the impact plate on the orientation of the impact plate was identified. By inducing known changes to the impact plate orientation and implementing model-based estimation, a means of self-calibration of the sensing system was achieved. Three methods of model-based estimation were successfully demonstrated using data generated from the physics-based model. Additionally, these were further verified using data collected from discrete element modeling simulations, and experimental data collected in two fashions: using a full-scale replica of the mass flow sensing system, and using a small-scale, benchtop testing apparatus. Furthermore, the ability of the developed algorithm to update theoretical model parameters while simultaneously estimating mass flow rate was shown to enable the system to self-calibrate. This was argued to allow the system to accommodate different operating conditions that may be encountered during combine harvesting, such as changes in crop moisture, grain variety, and aging of combine components.ii

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Section: Discussionmentioning

confidence: 99%

“…Furthermore, the percentage of the opening area of the auger available to the paddle of interest during these times is equivalent. It follows that 4) which simplifies to…”

Section: Discussionmentioning

confidence: 99%

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Self-Calibrating Mass Flow Sensor

Reinke

Dankowicz

2012

Volume 6: 1st Biennial International Conference on Dynamics for Design; 14th International Conference on Advanced Vehicle Techn

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“…There are mainly four types of grain flow sensor: impact-type [1], ray-type [2], volume-type [3], and weight-type [4]. Most commercial yield monitoring systems rely on impact-type for security, reliability, and simple structure.…”

Section: Introductionmentioning

confidence: 99%

Using an Improved Artificial Bee Colony Algorithm for Parameter Estimation of a Dynamic Grain Flow Model

Liang

Gao

2018

Mathematical Problems in Engineering

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An effective method is proposed to estimate the parameters of a dynamic grain flow model (DGFM). To this end, an improved artificial bee colony (IABC) algorithm is used to estimate unknown parameters of DGFM with minimizing a given objective function. A comparative study of the performance of the IABC algorithm and the other ABC variants on several benchmark functions is carried out, and the results present a significant improvement in performance over the other ABC variants. The practical application performance of the IABC is compared to that of the nonlinear least squares (NLS), particle swarm optimization (PSO), and genetic algorithm (GA). The compared results demonstrate that IABC algorithm is more accurate and effective for the parameter estimation of DGFM than the other algorithms.

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“…Mass flow measurement principles include a plate impact sensor , torque sensors mounted on the elevator paddles ), a torque sensor mounted on the clean grain elevator drive shaft (Zandonadi et al 2008), a diaphragm impact sensor (Schrock et al 1999), a pivoted-auger-fed device (Wagner 1998), and weighing of the clean grain auger (Colvin 1990). Indirect methods encompass a capacitive sensor (Borgelt 1993), an ultrasonic sensor (Borgelt 1993), and X-ray measurement principles (Arslan et al 2000).…”

Section: Introductionmentioning

confidence: 99%

A dynamic grain flow model for a mass flow yield sensor on a combine

et al. 2011

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A model is developed to describe the flow of grain through a clean grain elevator system on a combine in order to facilitate accurate mass flow rate estimation. The relationship between mass flow rate and impact force described by the model depends upon machine operational characteristics, mechanical interactions of the grain and the machine geometry, and material properties of the grain. The model was designed to be adaptable to varying grain conditions, such as those influenced by moisture content, by allowing free parameters of the model to be estimated through a nonlinear regression algorithm. Simulations were performed using discrete element modeling software and data was obtained from experiments conducted on a clean grain elevator system at the University of Kentucky Combine Yield Monitor Test Facility to determine the ability of the model to accurately estimate mass flow rate. The model estimated mass flow rate with a normalized root mean squared residual (NRMSR) less than 2% for discrete element modeling simulations. For experiments involving machine components, NRMSR values were less than 3% for corn at 14% moisture, less than 3% for corn at 21% moisture, and less than 5% for corn at 26% moisture.

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Grain flow measurements with X-ray techniques

Cited by 23 publications

References 3 publications

Self-Calibrating Mass Flow Sensor

Self-Calibrating Mass Flow Sensor

Using an Improved Artificial Bee Colony Algorithm for Parameter Estimation of a Dynamic Grain Flow Model

A dynamic grain flow model for a mass flow yield sensor on a combine

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