Development and Validation of simulation model for three point-hitch during agricultural operation

Jeon, Hyeon-Ho; Jung, Yeon-Jun; Siddique, Md. Abu Ayub; Nam, Kyu-Chul; Kim, Tae‐Bum; Choi, Chang-Hyun; Kim, Yong Joo

doi:10.13031/aim.201901032

Cited by 2 publications

(3 citation statements)

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“…In terms of the overall two-step validation process, there are a variety of agricultural machinery research applications that have performed experiments and used quantification methods to determine the level of acceptability between experimental and MBD simulation error: 1. As described by Jeon et al, the development and validation of a three-point hitch simulation model during an agricultural operation consisted of physically installing a six-component load cell on a three-point hitch and connecting it to a data logger, executing a single field experiment with specific test conditions, repeating that test in an MBD model, and comparing the physical and virtual timeseries load cell data (Jeon et al, 2019). This study arbitrarily concluded that there was a minimal difference between X-axis load cell experimental and simulation data and that the model was validated because there was only a 5% difference between the averages of the two signals.…”

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confidence: 99%

Validation Principles of Agricultural Machine Multibody Dynamics Models

Adams¹,

Darr²

2022

Journal of the ASABE

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HighlightsThis study focused on proper validation methodology for agricultural machine multibody dynamic models.Experimental design included categorizing subsystems, data acquisition, testing, and data pre-processing.Three error quantification metrics are proposed to summarize model performance and overall agreement.Abstract. Multibody dynamic (MBD) models are required to be accurate if they are to be used with confidence. To determine the overall accuracy of an agricultural machine MBD model, the model needs to be validated through proper experimental design principles and error analysis. At a high-level, the main methodology concepts for the experimental design involve categorizing dynamic subsystems, creating a data acquisition plan, determining and completing experimental testing, and pre-processing of physical and virtual time series datasets. This paper provides a methodology and example validation process for an MBD model of an agricultural self-propelled sprayer. As part of the experimental testing, a tip test revealed that the vertical center of gravity of the MBD model resulted in a 2.8% error from the physical machine. Furthermore, to summarize simulation model performance against physical data, three error quantification metrics for time-series data comparison are proposed: (1) correlation coefficient, (2) two-sample t-test and two-sample F-test, and (3) standard deviation of absolute error. For determining whether there is an acceptable level of agreement, this study proposes that the correlation coefficient should be greater than 0.7, the two-sample t-test and two-sample F-test should both pass (but with special consideration of the resulting confidence intervals if they fail), and that the standard deviation of absolute error should be less than 25% of the standard deviation of physical time series data. These error metrics, together with their defined sets of agreement criteria, resulted in detecting where the self-propelled sprayer system was experiencing validation issues and eventually the overall practical validation of the MBD model. Keywords: Agricultural machines, Confidence, Error metrics, Models, Multibody dynamics (MBD), Simulation, Validation.

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mentioning

confidence: 99%

Validation Principles of Agricultural Machine Multibody Dynamics Models

Adams¹,

Darr²

2022

Journal of the ASABE

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“…1. As described by Jeon et al, the development and validation of a three-point hitch simulation model during an agricultural operation consisted of physically installing a sixcomponent load cell on a three-point hitch and connecting it to a data logger, executing a single field experiment with specific test conditions, repeating that test in an MBD model, and comparing the physical and virtual timeseries load cell data (Jeon et al, 2019). This study concluded that there was a minimal difference between X-axis load cell experimental and simulation data because there was only a 5% difference between the averages of the two signals.…”

Section: Discussionmentioning

confidence: 99%

“…Historically, there have been a few studies (Jeon et al, 2019; focused on MBD agricultural machinery applications that have incorporated some level of validation, but the depth of the validation process is not adequate to conclude confidence in the model. The other challenge with these previous investigations is that the experiments performed for validation of the MBD model are too unique and subjective to the given machine and application.…”

Section: Validationmentioning

confidence: 99%

Multibody dynamics modeling, validation, and analysis of a mobile off-road vehicle

Adams

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Development and Validation of simulation model for three point-hitch during agricultural operation

Cited by 2 publications

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Validation Principles of Agricultural Machine Multibody Dynamics Models

Validation Principles of Agricultural Machine Multibody Dynamics Models

Multibody dynamics modeling, validation, and analysis of a mobile off-road vehicle

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