Kinematic and Dynamic Analysis of the Mechanism of a Vibrating Screen Type Potato Sorting Machine

Potato and onion sorting is a key technological stage in their production process. Minimizing tuber damage is a key criterion for evaluating the efficiency of sorting machines equipped with elastic coatings on their working surfaces designed to reduce impact loads. (Research purpose) The study aims to conduct kinematic and dynamic analysis of the drive mechanism of the Tolsma sorting machine’s working unit to assess its performance under the operating conditions of the AGROVER potato production cluster (Tashkent region, Republic of Uzbekistan). (Materials and methods) The methodology focuses on evaluating inertial loads which are critical to minimizing tuber damage during sorting, aligning with the broader goal of improving the machine efficiency. (Results and discussion) A graphic analysis was conducted to examine the vertical force acting on tubers during screen oscillation. The results show that the direction of this force closely aligns with gravity, leading to tuber clogging within the holes of the working surface. The minimal repulsive forces fail to ensure efficient produce movement, increasing the risk of damage due to elastic coating deformation and the oval shape of the tubers, which act as wedges. Eeven additional mechanisms do not fully resolve the problems of reduced productivity and tuber damage associated with inertial loads and screen geometry. (Conclusions) The findings support the results of kinematic and dynamic modeling. Inertial forces generated by the accelerated movement of the screens critically affect tubers-surfaces interaction, necessitating machine parameters optimization.

1. Bahadirov G., Sultanov T., Umarov B., Bakhadirov K. Advanced machine for sorting potatoes tubers. IOP Series: Materials Science and Engineering. 2020. 883. 012132 (In English). DOI: 10.1088/1757-899x/883/1/012132.

2. Bahadirov G., Umarov B., Obidov N. et al. Justification of the geometric dimensions of drum sorting machine. IOP Series: Earth and Environmental Science. 2021. Vol. 937. N3. 032043 (In English). DOI: 10.1088/1755-1315/937/3/032043.

3. Nie J., Wang Y., LI Y., Chao X. Artificial intelligence and digital twins in sustainable agriculture and forestry: a survey. Turkish Journal of Agriculture and Forestry. 2022. Vol. 46. N5. 5 (In English). DOI: 10.55730/1300-011X.3033.

4. Dorokhov A.S., Mosyakov M.A., Sazonov N.V. Automated line for post-harvest processing of root crops and potatoes. Agricultural Machinery and Technologies. 2020. Vol. 14. N1. 22- 26 (In Russian). DOI: 10.22314/2073-7599-2020-14-1-22-26.

5. Ivanov A.G., Erokhin M.N., Kazantsev S.P. et al. Improving the efficiency of screen type potato sorting machines by a modification of the sieve drive movement algorithm. Agricultural Machinery and Technologies. 2023. Vol. 17. N2. 13-19 (In Russian). DOI: 10.22314/2073-7599-2023-17-2-13-19.

6. Kryukov M.L., Zernov V.N., Kalinkin G.A. et al. Seed potatoes harvesting and transportation technology. Agricultural Machinery and Technologies. 2017. Vol. 2. 24-30 (In Russian). DOI: 10.22314/2073-7599-2017-2-24-30.

7. Jia Sh., Liu G., Jia J., Yang T. Development and application of the intelligent seed processing technology and equipment. Agricultural Machinery and Technologies. 2021. Vol. 15(4). 24-28 (In English). DOI: 10.22314/2073-7599-2021-15-4-24-28.

8. Khan R. Artificial intelligence and machine learning in food industries: A study. J Food Chem Nanotechnol. 2022. Vol. 7. N3. 60-67 (In English). DOI: 10.17756/jfcn.2021-114.

9. Danielak M., Przybył K., Koszela K. The need for machines for the nondestructive quality assessment of potatoes with the use of artificial intelligence methods and imaging techniques. Sensors. 2023. Vol. 23. N4. 1787 (In English). DOI: 10.3390/s23041787.

10. Haverkort A.J., Struik P.C., Linnemann A.R., Wiskerke J.S.C. On processing potato 3: survey of performances, productivity and losses in the supply chain. European Potato Journal. 2023. Vol. 66. N2. 385-427 (In English). DOI: 10.1007/s11540-022-09576-7.

11. Cortiello M., Sanfelici A., Milc J.A., Martini S. Genotype and plant biostimulant treatments influence tuber size and quality of potato grown in the pedoclimatic conditions in Northern Apennines in Italy. International Journal of Plant Production. 2024. 18(36). 1-21 (In English). DOI: 10.1007/s42106-024-00311-5.

12. Korotchenya V.M., Tsench Yu.S., Lobachevsky Ya.P. The machine system as a factor of scientific and technological progress in agro-industrial complex. Rossiiskaia selskokhoziaistvennaia nauka. 2024. N4. 67-72 (In Russian). DOI: 10.31857/S250026272404012.

13. Izmaylov A.Yu., Kolchin N.N., Lobachevskiy Ya.P., Kynev N.G. Modern technologies and special equipment for potato production. Agricultural Machinery and Technologies. 2015. N3. 43-47 (In Russian). EDN: TTLVUJ.

14. Orlando Guerrero I. J. et al. Embedded vision system controlled by dual multi-frequency tones. Proceedings ICITA. 2022. 593-603 (In English). DOI: 10.1007/978-981-19-9331-2_51.