Address: 17 Naberezhnaya Severnoy Dviny, Arkhangelsk 163002 Russian Federation. Northern (Arctic) Federal University named after M.V.Lomonosov. Office 1425

Phone / Fax: (818-2) 21-61-18
E-mail: forest@narfu.ru
http://lesnoizhurnal.ru/en/

RussianEnglish



Archive

Effective Positioning in Cutting Area of a Harvester Using Computer Modelling. P. 120–135

Версия для печати
Creative Commons License
These works are licensed under a Creative Commons Attribution 4.0 International License.

Makarenko A.V.

Complete text of the article:

Download article (pdf, 3.1MB )

UDС

630*326

DOI:

10.37482/0536-1036-2023-4-120-135

Abstract

An improvement of methods and models for preliminary estimation of the performance of harvesters in timber production intends to support better justification of the use of certain harvester types and technologies for specific natural and production conditions. It is possible to increase the accuracy of calculation models with a more complete and realistic description of the production environment and operational processes. The article presents a modelling of the harvester’s movements across cutting areas for selecting a position with the highest number of accessible trees. The investigational process involved the creation of an algorithm along with a simulation model and the statistical processing of the results. The problem-solving process required consideration of the tree’s distribution within the cutting region. The data arrays, which were necessary for efficiency evaluation in the simulation model and its software implementation, were the following: the distance between the working stands of the machine with the maximum number of available trees, the time of cyclic processing, and the number of trees in the area. The results of the statistical analysis of the data are presented with justification from the theoretical laws of probability distribution. The choice of machine working stands, which considered the arrangement of the trees, was estimated using the productivity per hour index. The index was calculated with a constant distance parameter that is equal to the difference between the maximum and the minimum manipulator’s movement, and it was also calculated for a stand with a maximum number of accessible trees. The calculation function for the index involves random variables that characterize the working conditions of the stand. The index itself is conceded as a random variable. The values for it were found by statistical data processing. A comparison of the values determined a high probability of a significant increase in the productivity of the harvester. At the stand with the maximum number of trees, it is estimated at around 8 % with a standard deviation of 0.199.

Authors

Andrey V. Makarenko, Candidate of Engineering, Assoc. Prof.;
ResearcherID: GON-8614-2022, ORCID: https://orcid.org/0000-0003-3889-9827

Affiliation

Mytishchi Branch of Bauman Moscow State Technical University, ul. 1-ya Institutskaya, 1, Mytishchi, Moscow Region, 141005, Russian Federation; makarenko@mgul.ac.ru

Keywords

working stand, harvesting, computer modelling, time of cyclic processing, random variable, productivity of a machine

For citation

Makarenko A.V. Effective Positioning in Cutting Area of a Harvester Using Computer Modelling. Lesnoy Zhurnal = Russian Forestry Journal, 2023, no. 4, pp. 120–135. (In Russ.). https://doi.org/10.37482/0536-1036-2023-4-120-135

References

  1. Azarenok V.A. Ecologically Harvesting of Mature and Overmature Stands in the Implementation of the Concept of Conservation of Forest Ecosystems (A Case Study of Sverdlovsk Region): Doc. Agric. Sci. Diss. Abs. Yekaterinburg, 2012. 40 p. (In Russ.).

  2. Baranovskiy V.A., Nekrasov R.M. Machine Systems for Logging. Moscow, Lesnaya promyshlennost’ Publ., 1977. 248 p. (In Russ.).

  3. Gerts E.F. Assessment of Forest Management Technology on Logging Operations. Yekaterinburg, USFEU Publ., 2003. 120 p. (In Russ.).

  4. Grigoryev I.V., Redkin A.K., Valyazhenkov V.D., Matrosov A.V. Technology and Equipment of Timber Industries. Technology and Machines of Logging Operations. Saint Petersburg, SPbGLTA Publ., 2010. 331 p. (In Russ.).

  5. Grushin M.A. Tree Crown Recognition on High Resolution Images. Lesnoy vestnik = Forest Bulletin, 2012, no. 6, pp. 149–154. (In Russ.).

  6. Zakharov V. Intelligent Control Systems for Forest Machines. LesPromInform, 2015, vol. 6, no. 112, pp. 72–78. (In Russ.).

  7. Ilchukov S.V. Dynamics of the Horizontal Structure of Derived Deciduous Plantings. Lesnoy Zhurnal = Russian Forestry Journal, 2003, no. 6, pp. 29–34. (In Russ.). http://lesnoizhurnal.ru/upload/iblock/873/873db22985ff48dfbb622e8cb66934e8.pdf

  8. Kalyashov V.A. Substantiation of Rational Technology of Non-Continuous Logging when Harvesting Sortings by Multi-Operation Machines: Cand. Eng. Sci. Diss. Abs. Saint Petersburg, 2004. 20 p. (In Russ.).

  9. Kapustin V.A. The Number of Trees Cuttings from one Stand by a Feller-Buncher. Lesnoy Zhurnal = Russian Forestry Journal, 1985, no. 2, pp. 32–36. (In Russ.). http://lesnoizhurnal.ru/upload/iblock/5ae/32_36.pdf

  10. Makarenko A.V., Redkin A.K. The Influence of Technological Solutions on the Conditions and Efficiency of Harvesters. Lesnoy vestnik = Forest Bulletin, 2014, no. 2-S, pp. 7–11. (In Russ.).

  11. Matveiko A.P. Low-Waste and Zero-Waste Technologies in Forestry and Timber Industry. Minsk, BSTU Publ., 1999. 84 p. (In Russ.).

  12. Menshikov V.N. Fundamentals of Forest Harvesting Technology with Preservation and Reproduction of the Natural Environment. Leningrad, Leningrad State University Publ., 1987. 220 p. (In Russ.).

  13. Nikolenko S., Kadurin A., Arkhangelskaya E. Deep Learning. Dipping into the World of Neural Networks. Saint Petersburg, Piter Publ., 2019. 480 p. (In Russ.).

  14. Secretenko O.P., Grabarnik P.Ya. Analysis of Tree Stand Horizontal Structure Using Random Point Field Methods. Sibirskij Lesnoj Zurnal = Siberian Journal of Forest Science, 2015, no. 3. pp. 32–44. (In Russ.). https://doi.org/10.15372/SJFS20150304

  15. Sukhikh V.I. Aerospace Methods in Forestry and Landscape Construction. Yoshkar-Ola, MarSTU Publ., 2005. 392 p. (In Russ.).

  16. Shegelman I.R., Budnik P.V., Baklagin V.N. Computer Experiment Technique for Determining the Load Capacity and Dimensions of the Carrying Compartment of a Forest Vehicle. Lesnoy Zhurnal = Russian Forestry Journal, 2019, no. 6, pp. 160–173. (In Russ.). https://doi.org/10.17238/issn0536-1036.2019.6.160

  17. Shirnin Yu.A. Modelling and Development of Optimal Technological Processes of Logging Operations (for Conditions of Volgo-Vyatsky Forest-Economic Region): Dr. Eng. Sci. Diss. Abs. Moscow, 1993. 42 p. (In Russ.).

  18. Shirnin Yu.A., Onuchin E. M. Simulation of Movement of Multi-Function Logging Machine. Lesnoy Zhurnal = Russian Forestry Journal, 2003, no. 4, pp. 66–72. (In Russ.). http://lesnoizhurnal.ru/upload/iblock/a6c/a6c9e8eb8c696f395f0f44a8cf739672.pdf

  19. Ackerman S.A., Talbot B., Astrup R. The Effect of Tree and Harvester Size on Productivity and Harvester Investment Decisions. International Journal of Forest Engineering, 2021, vol. 33, no. 1, pp. 22–32. https://doi.org/10.1080/14942119.2021.1981046

  20. Bilici E. Investigation of Feller-Buncher Performance Using Weibull Distribution. Forests, 2021, vol. 12, iss. 3, pp. 284. https://doi.org/10.3390/f12030284

  21. Liski E., Jounela P., Korpunen H., Sosa A., Lindroos O., Jylhä P. Modeling the Productivity of Mechanized CTL Harvesting with Statistical Machine Learning Methods. International Journal of Forest Engineering, 2020, vol. 31, iss. 3, pp. 253–262. https://doi.org/10.1080/14942119.2020.1820750

  22. Makarenko A.V., Redkin A.K., Bykovsky M.A., Shadrin A.A. Using Space Images of Forest Territories for Their Statistical Analysis. Proceedings of the AIP Conference Dedicated to the Memory of Academician S.P. Korolev and Other Outstanding Russian Scientists – Pioneers of Space Exploration. AIP Publishing Publ., 2019, vol. 2171, iss. 1, pp. 158–169. https://doi.org/10.1063/1.5133252

  23. Miyajima R.H., Fenner P.T., Batistela G.C., Simões D. Technical-Economic Analysis of Grapple Saw: A Stochastic Approach. Croatian Journal of Forest Engineering, 2020, vol. 41, iss. 2, pp. 219–229. https://doi.org/10.5552/crojfe.2020.681

  24. Noordermeer L., Sørngård E., Astrup R., Næsset E., Gobakken T. Coupling a Differential Global Navigation Satellite System to a Cut-To-Length Harvester Operating System Enables Precise Positioning of Harvested Trees. International Journal of Forest Engineering, 2021, vol. 32, no. 2, pp. 119–127. https://doi.org/10.1080/14942119.2021.1899686

  25. Wang J., LeDoux C.B., Li Y. Simulating Cut-to-Length Harvesting Operations in Appalachian Hardwoods. International Journal of Forest Engineering, 2005, vol. 16, no. 2, pp. 11–27. https://doi.org/10.1080/14942119.2005.10702510



 

Make a Submission


ADP_cert_2024.png

Lesnoy Zhurnal (Russian Forestry Journal) was awarded the "Seal of Recognition for Active Data Provider of the Year 2024"

INDEXED IN: 

scopus.jpg

DOAJ_logo-colour.png

logotype.png

Логотип.png