https://doi.org/10.4081/jae.2025.1610
Prediction model of PTO shaft fatigue damage considering sandy loam and loam in rotary-tillage operation
Published: 24 January 2025
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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In this study, the fatigue damage to a power takeoff (PTO) shaft was evaluated under various operating conditions in rotary-tillage operations, considering soil strength and texture. Pearson correlation analysis was conducted to identify the significant variables influencing PTO shaft fatigue damage, and a prediction formula was derived through regression analysis using these variables. The PTO shaft exhibited increased shear stress with higher transmission gear stages, PTO gear stages, or soil properties, including strength and texture. The fatigue damage increased with higher transmission gear stages and soil strength while decreasing with higher PTO gear stages. Notably, as the PTO gear stage increased, the mean stress increased; however, the stress amplitude and equivalent completely reversed stress significantly reduced fatigue damage. Statistical analyses revealed a strong correlation between PTO shaft fatigue damage and factors such as tractor travel speed, PTO shaft power consumption, PTO shaft rotational speed properties, including strength and texture. The developed prediction equation, incorporating all significant variables, demonstrated, with a coefficient of determination (R²) of 0.93 and a root mean square error (RMSE) of 2.94×10-9. This equation effectively identifies trends in PTO shaft fatigue damage based on key operational variables. Furthermore, the findings emphasize the critical role of soil texture in assessing PTO shaft fatigue damage.
Al-Dosary, N.M.N., Aboukarima, A.M., Al-Hamed, S.A., 2022. Evaluation of artificial neural network to model performance attributes of a mechanization unit (tractor-chisel plow) under different working variables. Agriculture (Basel) 12:840.
DOI: https://doi.org/10.3390/agriculture12060840
Amzallag, C., Gerey, J.P., Robert, J.L., Bahuaud, J., 1994. Standardization of the rainflow counting method for fatigue analysis. Int. J. Fatigue. 16:287–293.
DOI: https://doi.org/10.1016/0142-1123(94)90343-3
Anthes, R.J., 1997. Modified rainflow counting keeping the load sequence. Int. J. Fatigue. 19:529–535.
DOI: https://doi.org/10.1016/S0142-1123(97)00078-9
Asare, E., Segarra, E., 2018. Adoption and extent of adoption of georeferenced grid soil sampling technology by cotton producers in the southern US. Precision Agric. 19:992–1010.
DOI: https://doi.org/10.1007/s11119-018-9568-3
Baek, S.M., Kim, W.S., Park, S.U., Kim, Y.J., 2019. Analysis of equivalent torque of 78-kW agricultural tractor during rotary tillage. J. Korea Inst. Inf. Electron. Commun. Technol. 12:359-365.
Blanchet, F.G., Legendre, P., Borcard, D., 2008. Forward selection of explanatory variables. Ecology 89:2623–2632..
DOI: https://doi.org/10.1890/07-0986.1
Chai, T., Draxler, R.R., 2014. Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geosci. Model Dev. 7:1247_1250.
DOI: https://doi.org/10.5194/gmd-7-1247-2014
Grubisic, V., 1994. Determination of load spectra for design and testing. Int. J. Veh. Des. 15:8–26.
DOI: https://doi.org/10.1504/IJVD.1994.061902
Hensh, S., Tewari, V.K., Upadhyay, G., 2021. An instrumentation system to measure the loads acting on the tractor PTO bearing during rotary tillage. J. Terramech. 96:1-10.
DOI: https://doi.org/10.1016/j.jterra.2021.04.004
Hodson, T.O., 2022. Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not. Geosci. Model Dev. 15:5481_5487.
DOI: https://doi.org/10.5194/gmd-15-5481-2022
Hwang, S.J., Kim, J.H., Jang, M.K., Nam, J.S., 2022. Correlation between tractor variables and loan support limit in South Korea through regression analysis. J. Biosyst. Eng. 47:402_408.
DOI: https://doi.org/10.1007/s42853-022-00155-9
Hwang, S.J., Kim, J.H., Jang, M.K., Nam, J.S., 2022. Prediction model of loan support limit of plows and rotavators used in South Korea by regression analysis. J. Biosyst. Eng. 47:409_416.
DOI: https://doi.org/10.1007/s42853-022-00154-w
Jabro, J.D., Stevens, W.B., Iversen, W.M., Sainju, U.M., Allen, B.L., 2021. Soil cone index and bulk density of a sandy loam under no-till and conventional tillage in a corn-soybean rotation. Soil Till. Res. 206:104842.
DOI: https://doi.org/10.1016/j.still.2020.104842
Kamal, M., Rahman, M.M., 2018. Advances in fatigue life modeling: A review. Renew. Sustain. Energy Rev. 82:940_949.
DOI: https://doi.org/10.1016/j.rser.2017.09.047
Karunasingha, D.S.K., 2022. Root mean square error or mean absolute error? Use their ratio as well. Inf. Sci. 585:609_629.
DOI: https://doi.org/10.1016/j.ins.2021.11.036
Kerry, R., Oliver, M.A., 2004. Average variograms to guide soil sampling. Int. J. Appl. Earth Obs. Geoinf. 5:307_325.
DOI: https://doi.org/10.1016/j.jag.2004.07.005
Kim, D.C., Nam, J.S., Kim, M.H., Choe, J.S., Inoue, E., Okayasu, T., Kim, D.C., 2013a. Analysis of the tillage and power consumption characteristics of a crank–type rotavator according to the tillage blade shape. J. Fac. Agric. Kyushu Univ. 58:319_328.
DOI: https://doi.org/10.5109/27363
Kim, J.G., Kim, Y.J., Kim, J.H., Shin, B.S., Nam, J.S., 2018. Consumed-power and load characteristics of a tillage operation in an upland field in Republic of Korea. J. Biosyst. Eng. 43:83-93.
Kim, J.G., Park, J.S., Choi, K.J., Lee, D.K., Shin, M.S., Oh, J.Y., Nam, J.S., 2020. Analysis of agricultural tractor transmission using actual farm workload. J. Korean Soc. Manuf. Process Eng. 19:42-48.
DOI: https://doi.org/10.14775/ksmpe.2020.19.11.042
Kim, M.H., Nam, J.S., Kim, D.C., 2013b. Comparison of tillage and loads characteristics of three types of rotavators: Rotary-type, crank-type, and plow-type. Biosyst. Eng. 38:73-80.
DOI: https://doi.org/10.5307/JBE.2013.38.2.073
Kim, S.J., Gim, D.H., Jang, M.K., Hwang, S.J., Kim, J.H., Yang, Y.J., Nam, J.S., 2023. Development of regression model for predicting the maximum static friction force of tractors with a front-end loader. J. Biosyst. Eng. 48:329-338.
DOI: https://doi.org/10.1007/s42853-023-00194-w
Kim, S.S., Lee, Y.S., Woo, J.K., 1997. Tilling load characteristic analysis according to the shape factors of rotary blade. Biosyst. Eng. 06c:18-24.
Kim, W.S., Kim, Y.J., Baek, S.M., Beak, S.Y., Moon, S.P., Lee, N.G., et al 2020. Effect of the cone index on the work load of the agricultural tractor. J. Drive Control. 17:9–18.
Kim, W.S., Kim, Y.J., Park, S.U., Hong, S.J., Kim, Y.S., 2019. Evaluation of PTO severeness for 78-kW-class tractor according to disk plow tillage and rotary tillage. J. Drive Control. 16:23-31.
Kim, Y.J., Chung, S.O., Choi, C.H., Lee, D.H., 2011. Evaluation of tractor PTO severeness during rotary tillage operation. Biosyst. Eng. 36:163–170.
DOI: https://doi.org/10.5307/JBE.2011.36.3.163
Kim, Y.S., Bae, B.M., Kim, W.S., Kim, Y.J., Lee, S.D., Kim, T.J., 2023. Working load analysis of a 42 kW class agricultural tractor according to tillage type and gear selection during rotary tillage operation. Agriculture (Basel) 13:1556.
DOI: https://doi.org/10.3390/agriculture13081556
Kim, Y.S., Kim, W.S., Baek, S.Y., Baek, S.M., Kim, Y.J., Lee, S.D., Kim, Y.J., 2020. Analysis of tillage depth and gear selection for mechanical load and fuel efficiency of an agricultural tractor using an agricultural field measuring system. Sensors (Basel). 20:2450.
DOI: https://doi.org/10.3390/s20092450
Kumar, A., Chen, Y., Sadek, M.A.A., Rahman, S., 2012. Soil cone index in relation to soil texture, moisture content, and bulk density for no-tillage and conventional tillage. Agric. Eng. Int. CIGR J. 14:26–37.
Kumari, A., Raheman, H., 2023. Tillage operation with a tractor drawn rotavator using an embedded advisory system for minimizing fuel consumption. J. Biosyst. Eng. 48:487–502.
DOI: https://doi.org/10.1007/s42853-023-00207-8
Lee, D.H., Kim, Y.J., Chung, S.O., Choi, C.H., Lee, K.H., Shin, B.S., 2015. Analysis of the PTO load of a 75kW agricultural tractor during rotary tillage and baler operation in Korean upland fields. J. Terramech. 60:75–83.
DOI: https://doi.org/10.1016/j.jterra.2015.06.002
Lee, W.S., Lee, H.W., 1998. A study on the prediction of the fatigue life of a lug through the finite element analysis. J. Korean Soc. Precis. Eng. 15:88–95.
Lee, Y.S., Lee, J.H., Kwon, Y.N., Ishikawa, T., 2004. Effects of material properties on elastic characteristics of cold forged part. Mater. Sci. Forum. 449-452: 853–856.
DOI: https://doi.org/10.4028/www.scientific.net/MSF.449-452.853
Libin, Z., Jiandong, J., Yanbiao, L., 2010. Agricultural rotavator power requirement optimization using multi-objective probability parameter optimization. Int. Agric. Eng. J. 19:15-22.
Mallarino, A.P., Wittry, D.J., 2004. Efficacy of grid and zone soil sampling approaches for site-specific assessment of phosphorus, potassium, pH, and organic matter. Precis. Agric. 5:131-144.
DOI: https://doi.org/10.1023/B:PRAG.0000022358.24102.1b
Myung, B.S., Lee, H.D., 2009. Research on the actual condition of rotary tilling & rotary power requirement in the central area. J. Korean Soc. Ind. Converg. 12:79-83.
Naderloo, L., Alimadani, R., Akram, A., Javadikia, P., Khanghah, H.Z., 2009. Tillage depth and forward speed effects on draft of three primary tillage implements in clay loam soil. J. Food Agric. Environ. 7:382-385.
Nam, J.S., Kim, D.C., Kim, M.H., Kim, D.C., 2012. Tillage characteristics estimation of crank-type and rotary-type rotavators by motion analysis of tillage blades. Biosyst. Eng. 37:279–286.
DOI: https://doi.org/10.5307/JBE.2012.37.5.279
Natpukkana, P., Pakinsee, S., Boonmapat, S., Mitsomwang, P., Borrisutthekul, R., Panuwannakorn, R., Khoa-Phong, L., 2018. Investigation of notch shear cutting for JIS SCM420 steel wire rod. IOP Conf. Ser. Mater. Sci. Eng. 436:012013.
DOI: https://doi.org/10.1088/1757-899X/436/1/012013
Park, S.H., Lee, D.H., Kim, H.J., Lee, C.S., Cho, S.C., Kwak, T.Y., 2002. Development of dry paddy seeder of strip tillage. Biosyst. Eng. 27:25–32.
DOI: https://doi.org/10.5307/JBE.2002.27.1.025
Rousson, V., Goşoniu, N.F., 2007. An R-square coefficient based on final prediction error. Stat. Methodol. 4:331-340.
DOI: https://doi.org/10.1016/j.stamet.2006.11.004
Ryu, M.J., Chung, S.O., Kim, Y.J., Lee, D.H., Choi, C.H., Lee, K.H., 2012. FFT analysis of load data during field operations using a 75-kW agricultural tractor. Korean J. Agric. Sci. 40:53-59.
DOI: https://doi.org/10.7744/cnujas.2013.40.1.053
Ryu, M.J., Kang, S.W., Chung, S.O., Kim, Y.J., Lee, D.H., Choi, C.H., 2013. Load characteristics of 30 and 75-kW agricultural tractors during field operations. Proc. American Society of Agricultural and Biological Engineers, Kansas City, July 21–24, paper number 131590237,
Santecchia, E., Hamouda, A.M.S., Musharavati, F., Zalnezhad, E., Cabibbo, M., El Mehtedi, M., Spigarelli, S., 2016. A review on fatigue life prediction methods for metals. Adv. Mater. Sci. Eng. 2016:1–26.
DOI: https://doi.org/10.1155/2016/9573524
Shigley, J.E., Budynas, R.G., Nisbett, J.K., 2020. Mechanical engineering design. 11th ed. McGraw-Hill, p. 212–256.
ASABE Standard S313.3, 2018. Soil cone penetrometer. St. Joseph, American Society of Agricultural and Biological Engineers.
ASABE Standard EP542.1, 2019. Procedures for using and reporting data obtained with the soil cone penetrometer. St. Joseph, American Society of Agricultural and Biological Engineers.
Sutter, J.M., Kalivas, J.H., 1993. Comparison of forward selection, backward elimination, and generalized simulated annealing for variable selection. Microchem. J. 47:60–66.
DOI: https://doi.org/10.1006/mchj.1993.1012
Tan, K.H., 2005. Soil sampling, preparation, and analysis. Boca Raton, CRC Press.
DOI: https://doi.org/10.1201/9781482274769
Thompson, B., 1995. Stepwise regression and stepwise discriminant analysis need not apply here: A guidelines editorial. Educ. Psychol. Meas. 55:525–534.
DOI: https://doi.org/10.1177/0013164495055004001
Wollenhaupt, N.C., Wolkowski, R.P., 1994. Grid soil sampling. Better Crops 78:6–9.
Yadav, R.K., Singh, P., Singh, S.K., 2017. Performance analysis of rotavator and other tillage implement driven by the tractor. Int. J. Agric. Eng. 10:590–594.
DOI: https://doi.org/10.15740/HAS/IJAE/10.2/590-594
Supporting Agencies
Innovative Human Resource Development for Local Intellectualization program through the Institute of Information & Communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (IITP-2024-RS-2023-00260267, 50), Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry(IPET) through Machinery Mechanization Technology Development Program for Field Farming Program, grant funded by Ministry of Agriculture, Food and Rural Affairs(MAFRA)(RS-2023-00235957, 50)How to Cite
Jang, M.-K. (2025) “Prediction model of PTO shaft fatigue damage considering sandy loam and loam in rotary-tillage operation”, Journal of Agricultural Engineering. doi: 10.4081/jae.2025.1610.
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