Advertisement

Natural and Energy Resource Saving Based on the Development of Technology for Profile Milling of Wood Waste

  • A. A. FominEmail author
  • R. V. Yudin
  • A. R. Sadrtdinov
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

It is noted that the preservation of forest resources in the conditions of a permanently developing world-wide technological civilization is a major scientific and national economic problem. The results of the analysis of the current state of profile milling of wood and non-technological large sawmill waste are presented, and it is indicated that due attention was not paid to profile milling with a shaped tool. The technology and equipment have been developed with programmed control for the mechanical processing of large wood waste, equipped with an automatic control system for the working feed of a workpiece and having passed certification tests, as well as production approbation. The comparative tests of equipment with an automatic control system are presented, confirming the possibility of increasing the productivity of the profile milling process for workpieces with variable allowance for processing. The proposed equipment and technology allow not only saving material and energy resources in the process of mechanical processing of wood waste in the form of slabs, but also contributing to the preservation of forests due to deeper wood processing.

Keywords

Wood waste Profile milling Technologically unfeasible workpiece Milling machine Automatic control system Shaping cutter Feed rate 

References

  1. 1.
    Fomin AA et al (2016) Mechanical treatment of raw waste lumber an effective way to preserve the ecology and resources. IOP Conf Ser Mater Sci Eng 142(1):012091.  https://doi.org/10.1088/1757-899X/142/1/012091MathSciNetCrossRefGoogle Scholar
  2. 2.
    Su X, Wang G, Yu J, Jiang F, Li J, Rong Y (2016) Predictive model of milling force for complex profile milling. Int J Adv Manuf Technol 87(5):1653–1662CrossRefGoogle Scholar
  3. 3.
    Ohuchi T, Murase YJ (2005) Milling of wood and wood-based materials with a computerized numerically controlled router IV: development of automatic measurement system for cutting edge profile of throw-away type straight bit. Wood Sci 51:278Google Scholar
  4. 4.
    Banerjee A et al (2012) Geometry of chip formation in circular end milling. Int J Adv Manuf Technol 59(1–4):21–35.  https://doi.org/10.1007/s00170-011-3478-0
  5. 5.
    Kolenchenko OV (2010) Influence of the milling conditions on the deformation and quality of the machined surface. Russ Eng Res 30(8):839–844.  https://doi.org/10.3103/S1068798X1008023XCrossRefGoogle Scholar
  6. 6.
    Song G, Li J, Sun J (2013) Approach for modeling accurate undeformed chip thickness in milling operation. Int J Adv Manuf Technol 68:1429.  https://doi.org/10.1007/s00170-013-4932-y
  7. 7.
    Timerbaev NF, Sadrtdinov AR, Safin RG (2017) Software systems application for shafts strength analysis in mechanical engineering. Procedia Eng 206:1376–1381.  https://doi.org/10.1016/j.proeng.2017.10.648CrossRefGoogle Scholar
  8. 8.
    Li C, Chen X, Tang Y, Li L (2017) Selection of optimum parameters in multi-pass face milling for maximum energy efficiency and minimum production cost. J Clean Prod 140:1805–1818.  https://doi.org/10.1016/j.jclepro.2016.07.086CrossRefGoogle Scholar
  9. 9.
    Van Luttervelt CA et al (1998) Present situation and future trends in modelling of machining operations progress report of the CIRP working group ‘modelling of machining operations’. CIRP Ann Manuf Technol 47(2):587–626CrossRefGoogle Scholar
  10. 10.
    Stepanov VV et al (2017) Composite material for railroad tie. Solid State Phenom 265:587–591.  https://doi.org/10.4028/www.scientific.net/SSP.265.587CrossRefGoogle Scholar
  11. 11.
    Sadrtdinov AR et al (2016) The development of equipment for the disposal of solid organic waste and optimization of its operation. IOP Conf Ser Mater Sci Eng 142(1):012095.  https://doi.org/10.1088/1757-899X/142/1/012095CrossRefGoogle Scholar
  12. 12.
    Sadrtdinov AR et al (2016) The mathematical description of the gasification process of woody biomass in installations with a plasma heat source for producing synthesis gas. IOP Conf Ser Mater Sci Eng 124(1):012092.  https://doi.org/10.1088/1757-899X/124/1/012092CrossRefGoogle Scholar
  13. 13.
    Prosvirnikov DB et al (2017) Mechanization of continuous production of powdered cellulose technology. IOP Conf Ser: Mater Sci Eng 221(1):012009.  https://doi.org/10.1088/1755-1315/221/1/012009
  14. 14.
    Timerbaev NF, Ziatdinova DF, Safin RG, Sadrtdinov AR (2017) Gas purification system modeling in fatty acids removing from soapstock. In: Proceedings of 2017 international conference on industrial engineering, applications and manufacturing, ICIEAM 2017. Article no 8076418.  https://doi.org/10.1109/icieam.2017.8076418
  15. 15.
    Tuntsev DV et al (2016) The mathematical model of fast pyrolysis of wood waste. In: Proceedings of 2015, MEACS 2015. Article no 7414929.  https://doi.org/10.1109/meacs.2015
  16. 16.
    Saldaev VA et al (2016) Equipment for the production of wood-polymeric thermal insulation materials. IOP Conf Ser Mater Sci Eng 142(1):012097.  https://doi.org/10.1088/1757-899X/142/1/012097CrossRefGoogle Scholar
  17. 17.
    Fomin AA, Gusev VG (2013) Safe machining of blanks with nonuniform properties. Russ Eng Res 33(10):602–606.  https://doi.org/10.3103/S1068798X13100043CrossRefGoogle Scholar
  18. 18.
    Fomin AA, Gusev VG (2013) Spindle rigidity in milling blanks with nonuniform properties. Russ Eng Res 33(11):646–648.  https://doi.org/10.3103/S1068798X13110087CrossRefGoogle Scholar
  19. 19.
    Prosvirnikov DB, Baigildeeva EI, Sadrtdinov AR, Fomin AA (2017) Modelling heat and mass transfer processes in capillary-porous materials at their grinding by pressure release. In: Proceedings of 2017 international conference on industrial engineering, applications and manufacturing, ICIEAM 2017. Article no 8076443.  https://doi.org/10.1109/icieam.2017.8076443
  20. 20.
    Drapalyuk MV et al (2016) Modeling the digging process of tree root system by the mechanism with hydropulse drive. IOP Conf Ser: Mater Sci Eng 142:012090.  https://doi.org/10.1088/1757-899X/142/1/012090
  21. 21.
    Sadrtdinov AR et al (2016) The mathematical description of the gasification process of woody biomass in installations with a plasma heat source for producing synthesis gas. IOP Conf Ser: Mater Sci Eng 124:012092.  https://doi.org/10.1088/1757-899X/124/1/012092
  22. 22.
    Safin RG et al (2017) Technology of wood waste processing to obtain construction material. Solid State Phenom 265:245–249.  https://doi.org/10.4028/www.scientific.net/SSP.265.245CrossRefGoogle Scholar
  23. 23.
    Safin R et al (2016) A mathematical model of thermal decomposition of wood in conditions of fluidized bed. Acta Facultatis Xylologiae Zvolen res Publica Slovaca 58(2):141–148.  https://doi.org/10.17423/afx.2016.58.2.15MathSciNetCrossRefGoogle Scholar
  24. 24.
    Fomin AA (2017) Determining undeformed chip thickness models in milling and its verification during wood processing. Solid State Phenom 265:598–605CrossRefGoogle Scholar
  25. 25.
    Rezchikov AF, Kochetkov AV, Zakharov OV (2017) Mathematical models for estimating the degree of influence of major factors on performance and accuracy of coordinate measuring machines. MATEC Web Conf 129:01054CrossRefGoogle Scholar
  26. 26.
    Zakharov OV, Kochetkov AV (2016) Minimization of the systematic error in centerless measurement of the roundness of parts. Meas Tech 58:1317–1321CrossRefGoogle Scholar
  27. 27.
    Gerasimova AA, Radyuk AG (2014) The improvement of the surface quality of workpieces by coating. CIS Iron Steel Rev 9:33–35Google Scholar
  28. 28.
    Fomin AA, Gusev VG, Sattarova ZG (2018) Geometrical errors of surfaces milled with convex and concave profile tools. Solid State Phenom 284:281–288.  https://doi.org/10.4028/www.scientific.net/SSP.284.281CrossRefGoogle Scholar
  29. 29.
    Fomin AA (2017) Limiting product surface and its use in profile milling design operations. Solid State Phenom 265:672–678.  https://doi.org/10.4028/www.scientific.net/SSP.265.672CrossRefGoogle Scholar
  30. 30.
    Fomin AA (2017) Microgeometry of surfaces after profile milling with the use of automatic cutting control system. In: Proceedings of 2017 international conference on industrial engineering, applications and manufacturing, ICIEAM 2017. Article no 8076117.  https://doi.org/10.1109/icieam.2017.8076117
  31. 31.
    Namba Y, Tsuwa H (1977) Geometrical adaptive control in profile milling by CNC system. In: Proceedings of the seventeenth international machine tool design and research conference, Macmillan Education UK, pp 67–74.  https://doi.org/10.1007/978-1-349-81484-8_9
  32. 32.
    Volkov DI, Koryazhkin AA (2014) Adaptive belt grinding of gas-turbine blades. Russ Eng Res 34(1):37–40CrossRefGoogle Scholar
  33. 33.
    Nakagawa T, Yuzawa T, Sampei M, Hirata A (2017) Improvement in machining speed with working gap control in EDM milling. Precis Eng 47:303–310CrossRefGoogle Scholar
  34. 34.
    Stepanov YuS, Barsukov GV, Bishutin SG (2016) Technological fundamentals for efficiency control of hydroabrasive cutting. Procedia Eng 150:717–725.  https://doi.org/10.1016/j.proeng.2016.07.093CrossRefGoogle Scholar
  35. 35.
    Bardovsky A, Gerasimova A, Aydunbekov A (2018) The principles of the milling equipment improvement. MATEC Web Conf (224).  https://doi.org/10.1051/matecconf/201822401019
  36. 36.
    Gerasimova AA, Radyuk AG, Titlyanov AE (2016) Wear-resistant aluminum and chromonickel coatings at the narrow mold walls in continuous-casting machines. Steel Transl 46(7):458–462.  https://doi.org/10.3103/S0967091216070068CrossRefGoogle Scholar
  37. 37.
    Nekrasov RY, Tempel YA, Putilova US (2018) Precision CNC machining and ways to achieve it. MATEC Web Conf ICMTMTE 224:30Google Scholar
  38. 38.
    Grechnikov FV, Rezchikov AF, Zakharov OV (2018) Iterative method of adjusting the radius of the spherical probe of mobile coordinate-measuring machines when monitoring a rotation surface. Meas Tech 61:347–352CrossRefGoogle Scholar
  39. 39.
    Yemelyanov V, Tochilkina T, Vasilieva E, Nedelkin A, Shved E (2018) Computer diagnostics of the torpedo ladle cars. AIP Conf Proc 2034:020008.  https://doi.org/10.1063/1.5067351CrossRefGoogle Scholar
  40. 40.
    Yemelyanov VA (2014) Intelligent information technology of visual information processing for metals diagnostics. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 4:66–73Google Scholar
  41. 41.
    Konovalov S, Chen X, Sarychev V et al (2017) Mathematical modeling of the concentrated energy flow effect on metallic materials. Metals 7(1)Google Scholar
  42. 42.
    Timerbaev NF et al (2017) Application of software solutions for modeling and analysis of parameters of belt drive in engineering. IOP Conf Ser Earth Environ Sci 87(8):082047.  https://doi.org/10.1088/1755-1315/87/8/082047
  43. 43.
    Goch G et al (1999) Review of non-destructive measuring methods for the assessment of surface integrity: a survey of new measuring methods for coatings, layered structures and processed surfaces. Precis Eng 23(1):9–33.  https://doi.org/10.1016/S0141-6359(98)00021-XCrossRefGoogle Scholar
  44. 44.
    Popov IA, et al (2017) Heat transfer enhancement and critical heat fluxes in boiling of microfinned surfaces. High Temp 55(4):524.  https://doi.org/10.1134/S0018151X17030208
  45. 45.
    Nekrasov RY, Tempel YA, Tempel OA, Soloviev IV, Starikov AI (2017) Numerical studies to determine spatial deviations of a workpiece that occur when machining on CNC machines. MATEC Web Conf ICMTMTE 129:7Google Scholar
  46. 46.
    Nekrasov RY, Tempel YA, Starikov AI, Proskuryakov NA (2018) Fuzzy controllers in the adaptive control system of a CNC lathe. Russ Eng Res 38(3):220–222.  https://doi.org/10.3103/S1068798X18030188CrossRefGoogle Scholar
  47. 47.
    Prosvirnikov DB et al (2017) IOP Conf Ser Mater Sci Eng 221(1):012010.  https://doi.org/10.1088/1755-1315/221/1/012010
  48. 48.
    Sharkov OV, Koryagin SI, Velikanov NL (2016) Design models for shaping of tooth profile of external fine-module ratchet teeth. IOP Conf Ser Mater Sci Eng 124:012165.  https://doi.org/10.1088/1757-899X/124/1/012165CrossRefGoogle Scholar
  49. 49.
    Sharkov OV, Koryagin SI, Velikanov NL (2018) Shaping cutter original profile for fine-module ratchet teeth cutting. IOP Conf Ser Mater Sci Eng 327:042102.  https://doi.org/10.1088/1757-899X/327/4/042102CrossRefGoogle Scholar
  50. 50.
    Gromov VE, Kormyshev VE, Glezer AM et al (2018) Microstructure and wear properties of Hardox 450 steel surface modified by Fe-C-Cr-Nb-W powder wire surfacing and electron beam treatment. IOP Conf Ser Mater Sci Eng 411(1)Google Scholar
  51. 51.
    Fomin AA (2013) Kinematics of surface formation in milling. Russ Eng Res 33(11):660–662.  https://doi.org/10.3103/S1068798X13110099CrossRefGoogle Scholar
  52. 52.
    Zhou Y, Chen ZC, Tang J, Liu S (2016) An innovative approach to NC programming for accurate five-axis flank milling of spiral bevel or hypoid gears. Comput Aided Des 84:15–24MathSciNetCrossRefGoogle Scholar
  53. 53.
    Lashkov VA et al (2016) Modeling of a reduction zone of the gasifier installation. IOP Conf Ser: Mater Sci Eng 124:012111.  https://doi.org/10.1088/1757-899X/124/1/012111

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Vladimir State UniversityVladimirRussia
  2. 2.Voronezh State University of Forestry and Technologies named after G. F. MorozovVoronezhRussia
  3. 3.Kazan National Research Technological UniversityKazanRussia

Personalised recommendations