https://doi.org/10.4081/jae.2024.1685
Design of attitude-adjustable chassis and dynamic stress analysis of key components for crawler combine harvester
Published: 30 December 2024
Abstract Views: 39
PDF: 14
Publisher's note
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.
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.
Authors
To address the issues of leveling difficulties and poor stability of crawler combine harvesters in hilly and mountainous regions, this research analyzed the mechanical causes of overturning instability in crawler combine harvesters and designed an omnidirectional attitude adjustment chassis based on a five-bar mechanism. A 3D model was developed in SolidWorks, and coupled rigid-flexible simulations were performed using RecurDyn. Results showed that the chassis could achieve an overall lift, lateral adjustments and longitudinal adjustments (0-100 mm, -5.18° to 5.55° and -4.06° to 5.15° respectively), with maximum dynamic stress occurring on the left front and left rear rotational arms. A dynamic stress testing system was established to conduct response surface experiments. Field test results revealed that the primary factors affecting the maximum stress of the left front rotational arm were the grain tank loading mass, lateral adjustment angle, and longitudinal adjustment angle. For the left rear rotational arm, the order was the longitudinal adjustment angle, lateral adjustment angle, and grain tank loading mass. Validation tests showed that at a lateral adjustment angle of 3.61°, a longitudinal adjustment angle of 3.20°, and a grain tank load of 350 kg, the average maximum stresses were 483.19 MPa for the left front rotational arm and 188.95 MPa for the left rear rotational arm, with corresponding structural safety factors of 1.61 and 4.31, meeting strength requirements. This work provides methods for optimizing the design and reliability testing of agricultural machinery chassis with attitude adjustment functions in hilly terrains.
Adams, B., Darr, M. 2022. Validation principles of agricultural machine multibody dynamics models. J. ASABE 65:801-814.
DOI: https://doi.org/10.13031/ja.15045
Amirafshari, P., Brennan, F., Kolios, A. 2021. A fracture mechanics framework for optimising design and inspection of offshore wind turbine support structures against fatigue failure. Wind Energy Sci 6:677-699.
DOI: https://doi.org/10.5194/wes-6-677-2021
Belinsky, A., Ziganshin, B., Valiev, A., Haliullin, D., Galiev, I., Adigamov, N. 2019. Theoretical investigation of increasing efficiency of combine harvester operation on slopes. Eng.f Rural Develop. 18:206-213.
DOI: https://doi.org/10.22616/ERDev2019.18.N252
Chai, X., Hu, J., Ma, T., Liu, P., Shi, M., Zhu, L., Xu, L. 2024. Construction and characteristic analysis of dynamic stress coupling simulation models for the attitude-adjustable chassis of a combine harvester. Agronomy (Basel) 14:1874.
DOI: https://doi.org/10.3390/agronomy14081874
Jin, C., Yang, T., Liu, G., Wang, t., Chen, M., Liu, Z. 2020. Design and test of posture controlled chassis for caterpillar combine. Trans. CSAE 51:393-402.
Han, J., Kim, E., Moon, S., Lee, H., Kim, J., Park, Y. 2022. Fatigue integrity assessment for tractor-mounted garlic-onion harvester. J. Terramech. 100:1-10.
DOI: https://doi.org/10.1016/j.jterra.2021.11.005
Helian, B., Zheng, C., Bin, Y. 2021. Energy-saving and accurate motion control of a hydraulic actuator with uncertain negative loads. Chin. J. Aeronaut. 34:5:253-264.
DOI: https://doi.org/10.1016/j.cja.2020.12.025
Hu, J., Pan, J., Dai, B., Chai, X., Sun, Y., Xu, L. 2022. Development of an attitude adjustment crawler chassis for combine harvester and experiment of adaptive leveling system. Agronomy (Basel) 12:717.
DOI: https://doi.org/10.3390/agronomy12030717
İrsel, G., Altinbalik, M. T. 2018. Adaptation of tilt adjustment and tracking force automation system on a laser-controlled land leveling machine. Comput. Electron. Agric. 150:374-386.
DOI: https://doi.org/10.1016/j.compag.2018.04.021
Karliński, J., Stańco, M., Działak, P. 2023. The impact of the driving conditions on stress of load-bearing structure of self-propelled electric mining machine. Mater. Today Pro. 9: 791-798.
DOI: https://doi.org/10.1016/j.matpr.2023.07.137
Li, F., Liu, S., Mao, E., Du, Y., Lin, Y., Tian, J. 2016. Fatigue analysis of corn harvester frame based on power density. Trans. Chin. Soc. Agric. Eng. 32:34-40.
Lü, X., Liu, Z., Lü, X., Wang, X. 2024. Design and study on the leveling mechanism of the tractor body in hilly and mountainous areas. J. Eng. Design Technol. 22:679-689.
DOI: https://doi.org/10.1108/JEDT-11-2021-0617
Paul, A., Machavaram, R. 2024. Design of an adjustable chassis for a track type combine harvester. Cogent Eng. 11:2353811.
DOI: https://doi.org/10.1080/23311916.2024.2353811
Sirotin, P., Zhileykin, M., Sapegin, A., Zlenko, S. 2017. Prerequisites for the creation of an integrated system for leveling and cushioning framework of combine harvesters. Tract. Agr. Machin. 84: 21-28.
DOI: https://doi.org/10.17816/0321-4443-66335
Sun, J., Meng, C., Zhang, Y., Chu, G., Zhang, Y., Yang, F., Liu, Z. 2020. Design and physical model experiment of an attitude adjustment device for a crawler tractor in hilly and mountainous regions. Inform. Process. Agric. 7:466-478.
DOI: https://doi.org/10.1016/j.inpa.2020.02.004
Sun, Y., Xu, L., Jing, B., Chai, X., Li, Y. 2020. Development of a four-point adjustable lifting crawler chassis and experiments in a combine harvester. Comput. Electron. Agric. 173:105416.
DOI: https://doi.org/10.1016/j.compag.2020.105416
Si, D.Y., Fang, W.M., Zhang, Y. 2023. Flexible gear modeling and stress spectrum compilation of an artillery steering machine. J. Phys. Conf. Ser. 2528:012001.
DOI: https://doi.org/10.1088/1742-6596/2528/1/012001
Wang, Y., Yang, F., Pan, G., Liu, H., Liu, Z., Zhang, J. 2014. Design and testing of a small remote-control hillside tractor. J. ASABE 57:363-370.
DOI: https://doi.org/10.13031/trans.57.10229
Wang, Z., Ma, W., Wang, T., Liu, C. 2022. Body leveling control model establishment and experiment analysis. Appl. Eng. Agric. 38: 243-251.
DOI: https://doi.org/10.13031/aea.14501
Wang, Z., Xia, Y. 2019. Model establishment of body attitude adjustment system based on Backstepping control algorithm and automatic leveling technology. Cluster Comput. 22:14327-14337.
DOI: https://doi.org/10.1007/s10586-018-2292-y
Wang, Z., Yang, J., Liu, P., Long, X., Li, H., Wei, W. 2019. Development of an agricultural vehicle levelling system based on rapid active levelling. Biosyst. Eng. 186:337-348.
DOI: https://doi.org/10.1016/j.biosystemseng.2019.08.002
Wu, G., Wang, S., Zhang, A., Xiao, Y., Li, L., Yin, Y., Yan, B. 2023. Optimized design and experiment of a self-covering furrow opener for an automatic sweet potato seedling transplanting machine. Sustainability (Basel) 15:13091.
DOI: https://doi.org/10.3390/su151713091
Wang, C.J., Liu, Q., Yang, L. 2024. The MFBD-DEM coupling simulation approach for the investigation of granules screening effi-ciency in 4-DOF flip-flow screen. Granul. Matter. 26:5.
DOI: https://doi.org/10.1007/s10035-023-01380-5
Zhao, L.J., Yang, S.J., Zhang, H.N., Han, L.G. 2023. Fatigue life prediction of shearer rocker shell based on DEM-MFBD bidirectional coupling technology. Coal Sci. Tech. 51:S252-258.
Zhou, B., Chen, S., Hu, J., You, Y., Wang, D., Zhang, Q. 2024. Multi-body dynamics modeling and test of an articulated steering half-track tractor. Int. J. Agric. Biol. Eng. 16:124-133.
DOI: https://doi.org/10.25165/j.ijabe.20231606.8165
How to Cite
Hu, J. (2024) “Design of attitude-adjustable chassis and dynamic stress analysis of key components for crawler combine harvester”, Journal of Agricultural Engineering. doi: 10.4081/jae.2024.1685.
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Asiwan Kultongkham, Supakit Kumnon, Tawan Thintawornkul, Teeranoot Chanthasopeephan, The design of a force feedback soft gripper for tomato harvesting , Journal of Agricultural Engineering: Vol. 52 No. 1 (2021)
- Ziyan Fan, Lijun Li, Zicheng Gao, Fuzzy neural network PID control design of camellia fruit vibration picking manipulator , Journal of Agricultural Engineering: Vol. 54 No. 2 (2023)
- Alvaro Marucci, Danilo Monarca, Massimo Cecchini, Andrea Colantoni, Andrea Cappuccini, Analysis of internal shading degree to a prototype of dynamics photovoltaic greenhouse through simulation software , Journal of Agricultural Engineering: Vol. 46 No. 4 (2015)
- Francesco Bettella, Tamara Michelini, Vincenzo D'Agostino, Gian Battista Bischetti, The ability of tree stems to intercept debris flows in forested fan areas: A laboratory modelling study , Journal of Agricultural Engineering: Vol. 49 No. 1 (2018)
- Emanuele Cerruto, Giuseppe Manetto, Giampaolo Schillaci, Vibration produced by hand-held olive electrical harvesters , Journal of Agricultural Engineering: Vol. 43 No. 2 (2012)
- Alina Evelyn Badillo-Márquez, Jonathan J. Cid-Galiot, Rubén Posada-Gómez, Alberto Alfonso Aguilar-Lasserre, Automated system for the detection of risk in agricultural sugarcane harvesting using digital image processing and deep learning , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
- Frank Agyen Dwomoh, Xingye Zhu, Alexander Fordjour, Junping Liu, Shouqi Yuan, Hong Li, Structural design and performance characteristics of the fluidic sprinkler application technology for saving irrigation water: a review , Journal of Agricultural Engineering: Vol. 54 No. 2 (2023)
- Kamalesh Kanna S, Kumaraperumal Ramalingam, Pazhanivelan P, Jagadeeswaran R, Prabu P.C., YOLO deep learning algorithm for object detection in agriculture: a review , Journal of Agricultural Engineering: Vol. 55 No. 4 (2024)
- Ildar Gabitov, Samat Insafuddinov, Denis Kharisov, Elmir Gaysin, Timur Farhutdinov, Diagnostic method and device for evaluating and forecasting the technical condition of farm machinery in operation , Journal of Agricultural Engineering: Vol. 52 No. 4 (2021)
- Angela Calvo, Roberto Deboli, Christian Preti, Antonino De Maria, Daily exposure to hand arm vibration by different electric olive beaters , Journal of Agricultural Engineering: Vol. 45 No. 3 (2014)
<< < 2 3 4 5 6 7 8 9 10 11 > >>
You may also start an advanced similarity search for this article.
