Honing is an important technology for machining onboard system parts. The parts are usually made of difficult-to-machining materials, e.g., Inconel 718 superalloy. Honing can improve the finishing accuracy and surface quality. However, the selection of the honing parameters was primarily based on the results of a large number of experiments. Therefore, the establishment of a reliable model is needed to predict the honed surface roughness and morphology, and offers a theoretical direction for the choice of parameters. In the present study, a numerical simulation model was constructed for analysis of the honing process by Python. The oilstone, workpiece surface morphology and motion trajectory were discretized by Python, and the machined surface was obtained by trajectory interference. Firstly, based on the statistical analysis of the surface topography of oilstone, the shape of grains was simplified and the surface topography of oilstone was built accordingly. Then, the initial surface morphology of the workpiece was constructed and the trajectory of grains on the workpiece surface was analyzed, which showed the distribution of the removed material. Meanwhile, the plastic deformation of material was analyzed in the simulation model. The critical depth of three stages of contact between grains and workpiece was calculated by the theoretical formula: scratching, ploughing and cutting. By analyzing the distribution of bulge, the plastic deformation in ploughing and cutting stage was studied. Further, the simulated results of honed surface roughness and morphology were validated and agreed reasonably well with the honing experiment. Finally, the effects of honing process parameters, including grain size, tangential speed, axial speed, radial speed and abrasive volume percentage, on the surface roughness of the workpiece were analyzed by the simulation model.
The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00422-0
Chang-Yong Yang
,
Zhi Wang
,
Hao Su
,
Yu-Can Fu
,
Nian-Hui Zhang
,
Wen-Feng Ding
. Numerical analysis and experimental validation of surface roughness and morphology in honing of Inconel 718 nickel-based superalloy[J]. Advances in Manufacturing, 2023
, 11(1)
: 130
-142
.
DOI: 10.1007/s40436-022-00422-0
1. Choudhury IA, El-Baradie MA (1998) Machinability of nickel-base super alloys:a general review. J Mater Process Technol 77(1):278-284
2. Kong XW, Li B, Jin ZH et al (2011) Broaching performance of superalloy GH4169 based on FEM. J Mater Sci Technol 27(12):1178-1184
3. Pervaiz S, Rashid A, Deiab I et al (2014) Influence of tool materials on machinability of titanium- and nickel-based alloys:a review. Mater Manuf Processes 29(3):219-252
4. Joliet R, Kansteiner M, Kersting P (2015) A process model for force-controlled honing simulations. Procedia CIRP 28:46-51
5. Buj-Corral I, Vivancos-Calvet J, Coba-Salcedo M (2014) Modelling of surface finish and material removal rate in rough honing. Precis Eng 38(1):100-108
6. BŠhre D, Schmitt C, Moos U (2012) Analysis of the differences between force control and feed control strategies during the honing of bores. Procedia CIRP 1:377-381
7. Vrac DS, Sidjanin LP, Kovac PP et al (2012) The influence of honing process parameters on surface quality, productivity, cutting angle and coefficients of friction. Ind Lubr Tribol 64(2):77-83
8. Gafarov AM, Gafarov VA, Aliev GS (2011) Surface roughness in honing with dosed removal of surface layer. Russ Eng Res 31(10):1030-1033
9. Hecker RL, Liang SY (2003) Predictive modeling of surface roughness in grinding. Int J Mach Tools Manuf 43(8):755-761
10. Kumar S, Paul S (2012) Numerical modelling of ground surface topography:effect of traverse and helical superabrasive grinding with touch dressing. Prod Eng Res Devel 6(2):199-204
11. Chakrabarti S, Paul S (2008) Numerical modelling of surface topography in superabrasive grinding. Int J Adv Manuf Technol 39(1/2):29-38
12. Chen H, Tang J (2015) A model for prediction of surface roughness in ultrasonic-assisted grinding. Int J Adv Manuf Technol 77(1/4):643-651
13. Mahesh G, Muthu S, Devadasan SR (2015) Prediction of surface roughness of end milling operation using genetic algorithm. Int J Adv Manuf Technol 77(1/4):369-381
14. Goeldel B, El Mansori M, Dumur D (2012) Macroscopic simulation of the liner honing process. CIRP Ann 61(1):319-322
15. Goeldel B, Mansori ME, Dumur D (2013) Simulation of roughness and surface texture evolution at macroscopic scale during cylinder honing process. Procedia CIRP 8:27-32
16. Spencer A, Almqvist A, Larsson R (2011) A numerical model to investigate the effect of honing angle on the hydrodynamic lubrication between a combustion engine piston ring and cylinder liner. Proceed Institut Mech Eng Part J J Eng Tribol 225(7):683-689
17. Moos U, Bähre D (2015) Analysis of process forces for the precision honing of small bores. Procedia CIRP 31:387-392
18. Schmitt C, Bähre D (2014) Analysis of the process dynamics for the precision honing of bores. Procedia CIRP 17:692-697
19. Schmitt C, Bähre D (2013) An approach to the calculation of process forces during the precision honing of small bores. Procedia CIRP 7:282-287
20. Reizer R (2011) Simulation of 3D gaussian surface topography. Wear 271(3/4):539-543
21. Pawlus P (2008) Simulation of stratified surface topographies. Wear 264(5/6):457-463
22. Lu Y, Li J, Liang R et al (2020) Investigation on the effect of honing parameters on cylindricity of engine cylinder liner. Int J Adv Manuf Technol 111(11/12):3111-3122
23. Zhou X, Xi F (2002) Modeling and predicting surface roughness of the grinding process. Int J Mach Tools Manuf 42(8):969-977
24. Zhang X, Zhou Z (2020) Analytically predicating the multi-dimensional accuracy of the honed engine cylinder bore. J Tribol 142:1-23
25. Gassilloud R, Ballif C, Gasser P et al (2005) Deformation mechanisms of silicon during nanoscratching. Physica Status Solidi (a):Appl Mater Sci 202(15):2858-2869
26. Son S, Lim H, Ahn J (2006) The effect of vibration cutting on minimum cutting thickness. Int J Mach Tools Manuf 46(15):2066-2072
