Integration of abrasive machining theory and motion compensation strategies for honing of 9310 steel
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OpenAlex topics: Advanced Surface Polishing Techniques · Advanced machining processes and optimization · Tribology and Lubrication Engineering
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References (46)
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1. Yang CY, Wang Z, Su H et al (2023) Numerical analysis and experimental validation of surface roughness and morphology in honing of Inconel 718 nickel-based superalloy. Adv Manuf 11:130–142. https://doi.org/10.1007/s40436-022-00422-010.1007/s40436-022-00422-0 ↗
2. Yang C, Su H, Gao S et al (2021) Characterization and life prediction of single-pass honing tool for fuel injection nozzle. Chin J Aeronaut 34:225–240. https://doi.org/10.1016/j.cja.2020.08.00810.1016/j.cja.2020.08.008 ↗
3. Grabon W, Pawlus P, Wos S et al (2017) Effects of honed cylinder liner surface texture on tribological properties of piston ring-liner assembly in short time tests. Tribol Int 113:137–148. https://doi.org/10.1016/j.triboint.2016.11.02510.1016/j.triboint.2016.11.025 ↗
4. Mezghani S, Demirci I, Yousfi M et al (2013) Running-in wear modeling of honed surface for combustion engine cylinderliners. Wear 302:1360–1369. https://doi.org/10.1016/j.wear.2013.01.02610.1016/j.wear.2013.01.026 ↗
5. Goeldel B, El Mansori M, Dumur D (2012) Macroscopic simulation of the liner honing process. CIRP Ann 61:319–322. https://doi.org/10.1016/j.cirp.2012.03.04410.1016/j.cirp.2012.03.044 ↗
6. 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. https://doi.org/10.1016/j.procir.2013.06.06010.1016/j.procir.2013.06.060 ↗
7. Joliet R, Kansteiner M (2013) A high resolution surface model for the simulation of honing processes. Adv Mater Res 769:69–76. https://doi.org/10.4028/www.scientific.net/AMR.769.6910.4028/www.scientific.net/AMR.769.69 ↗
8. Joliet R, Kansteiner M, Kersting P (2015) A process model for force-controlled honing simulations. Procedia CIRP 28:46–51. https://doi.org/10.1016/j.procir.2015.04.00910.1016/j.procir.2015.04.009 ↗
9. Reizer R, Pawlus P, Wieczorowski M (2023) Simulation of plateau-honed cylinder liner surface texture creation using superimposition approach. Precis Eng 82:10–24. https://doi.org/10.1016/j.precisioneng.2023.02.00810.1016/j.precisioneng.2023.02.008 ↗
10. Zhou Z, Zhang X, Lv K et al (2018) Predicting microscale cross-hatched surface texture in engine cylinder bore. Procedia CIRP 71:272–278. https://doi.org/10.1016/j.procir.2018.05.00810.1016/j.procir.2018.05.008 ↗
11. Zhou Z, Zhang X, Lv K et al (2019) Simulating the sequential honing process of engine cylinder bore by modeling abrasives in honing stone. In: ASME 14th international manufacturing science and engineering conference, 10–14 June, Erie, Pennsylvania, USA. https://doi.org/10.1115/MSEC2019-304910.1115/MSEC2019-3049 ↗
12. 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 6:199–204. https://doi.org/10.1007/s11740-012-0370-110.1007/s11740-012-0370-1 ↗
13. Chen H, Tang J (2015) A model for prediction of surface roughness in ultrasonic-assisted grinding. Int J Adv Manuf Technol 77:643–651. https://doi.org/10.1007/s00170-014-6482-310.1007/s00170-014-6482-3 ↗
14. Tsybenko H, Xia W, Dehm G et al (2020) On the commensuration of plastic plowing at the microscale. Tribol Int 151:106477. https://doi.org/10.1016/j.triboint.2020.10647710.1016/j.triboint.2020.106477 ↗
15. Chen N, Li L, Wu J et al (2019) Research on the ploughing force in micro milling of soft-brittle crystals. Int J Mech Sci 155:315–322. https://doi.org/10.1016/j.ijmecsci.2019.03.00410.1016/j.ijmecsci.2019.03.004 ↗
16. Wan M, Ma YC, Feng J et al (2016) Study of static and dynamic ploughing mechanisms by establishing generalized model with static milling forces. Int J Mech Sci 114:120–131. https://doi.org/10.1016/j.ijmecsci.2016.05.01010.1016/j.ijmecsci.2016.05.010 ↗
17. Bhokse V, Chinchanikar S, Anerao P et al (2015) Experimental investigations on chip formation and plowing cutting forces during hard turning. Mater Today Proc 2:3268–3276. https://doi.org/10.1016/j.matpr.2015.07.13810.1016/j.matpr.2015.07.138 ↗
18. Laakso SVA, Agmell M, Stahl JE (2018) The mystery of missing feed force–the effect of friction models, flank wear and ploughing on feed force in metal cutting simulations. J Manuf Process 33:268–277. https://doi.org/10.1016/j.jmapro.2018.05.02410.1016/j.jmapro.2018.05.024 ↗
19. Yao Y, Liu G, Zhai J et al (2025) Chip formation and surface integrity investigation considering the minimum uncut chip thickness in micro-helical milling. J Market Res 36:2272–2283. https://doi.org/10.1016/j.jmrt.2025.03.30410.1016/j.jmrt.2025.03.304 ↗
20. Dong Z, Wang H, Qi Y et al (2023) Effects of minimum uncut chip thickness on tungsten nano-cutting mechanism. Int J Mech Sci 237:107790. https://doi.org/10.1016/j.ijmecsci.2022.10779010.1016/j.ijmecsci.2022.107790 ↗
21. Wojciechowski S, Krajewska-Śpiewak J, Maruda RW et al (2023) Study on ploughing phenomena in tool flank face-workpiece interface including tool wear effect during ball-end milling. Tribol Int 181:108313. https://doi.org/10.1016/j.triboint.2023.10831310.1016/j.triboint.2023.108313 ↗
22. de Paiva SG, de Oliveira D, Malcher L (2023) Numerical study of the minimum uncut chip thickness in micro-machining of Inconel 718 based on Johnson-Cook isothermal model. Int J Adv Manuf Technol 127:2707–2721. https://doi.org/10.1007/s00170-023-11573-010.1007/s00170-023-11573-0 ↗
23. Liu H, Guo Y, Li D et al (2021) Material removal mechanism of FCC single-crystalline materials at nano-scales: chip removal & ploughing. J Mater Process Technol 294:117106. https://doi.org/10.1016/j.jmatprotec.2021.11710610.1016/j.jmatprotec.2021.117106 ↗
24. Hu X, Xi C, Yu B (2014) Research on cylindricity prediction and process parameter optimization in honing based on response surface methodology. China Mech Eng 25:2098–2101, 2106. https://doi.org/10.3969/j.issn.1004-132X.2014.15.02110.3969/j.issn.1004-132X.2014.15.021 ↗
25. Buj-Corral I, Sender P, Luis-Pérez CJ (2023) Multi-objective optimization of tool wear, surface roughness, and material removal rate in finishing honing processes using adaptive neural fuzzy inference systems. Tribol Int 182:108354. https://doi.org/10.1016/j.triboint.2023.10835410.1016/j.triboint.2023.108354 ↗
26. Van AL, Nguyen TT (2022) An energy efficiency-based optimization of the rough honing process of hardened 5150 alloy. Proc Inst Mech Eng Part E J Process Mech Eng 237:336–349. https://doi.org/10.1177/0954408922110666110.1177/09544089221106661 ↗
27. Xi C, Hu X, Zhang Z (2011) Research for cylindricity prediction model of inner-hole honing. In: 2011 Second international conference on mechanic automation and control engineering, 15‒17 July, Hohhot, pp 1506–1509. https://doi.org/10.1109/MACE.2011.598723410.1109/MACE.2011.5987234 ↗
28. Nguyen TT, Vu TC, Duong QD (2020) Multi-responses optimization of finishing honing process for surface quality and production rate. J Braz Soc Mech Sci Eng 42:604. https://doi.org/10.1007/s40430-020-02690-y10.1007/s40430-020-02690-y ↗
29. Tang T, Liu C, Wang R (2021) Optimization of honing surface roughness of carburized holes based on GRA-RSM. Mach 9:291. https://doi.org/10.3390/machines911029110.3390/machines9110291 ↗
30. Chohan JS, Kumar R, Singh S et al (2023) Prediction of response parameters during magnetic assisted honing process using artificial neural network. AIP Conf Proc 2535:050002. https://doi.org/10.1063/5.011392410.1063/5.0113924 ↗
31. Sivatte-Adroer M, Llanas-Parra X, Buj-Corral I et al (2016) Indirect model for roughness in rough honing processes based on artificial neural networks. Precis Eng 43:505–513. https://doi.org/10.1016/j.precisioneng.2015.09.00410.1016/j.precisioneng.2015.09.004 ↗
32. Buj-Corral I, Sender P, Luis-Pérez CJ (2023) Modeling of surface roughness in honing processes by using fuzzy artificial neural networks. J Manuf Mater Process 7:23. https://doi.org/10.3390/jmmp701002310.3390/jmmp7010023 ↗
33. Zhang X, Zhang XP (2023) Free-floating dynamic material removal mechanism of the honing process. Int J Adv Manuf Technol 127:4473–4489. https://doi.org/10.1007/s00170-023-11760-z10.1007/s00170-023-11760-z ↗
34. Grezina AV, Igumnov LA, Metrikin VS et al (2024) Dynamics of honing of deep cylindrical holes with memory effects in the frictional interaction. In: Balandin D, Barkalov K, Meyerov I (eds) Mathematical modeling and supercomputer technologies, Springer Nature, Switzerland, Cham, pp 3–14. https://doi.org/10.1007/978-3-031-52470-7_110.1007/978-3-031-52470-7_1 ↗
35. Zhang X, Wang X, Wang D et al (2016) Methodology to improve the cylindricity of engine cylinder bore by honing. J Manuf Sci Eng 139(3):031008. https://doi.org/10.1115/1.403462210.1115/1.4034622 ↗
36. Gao L, Zhang X, Yao Z (2013) Trajectory reconstruction and precision control of cylinder bore honing of automobile engine. Mech Des and Res 29(1):75–79. https://doi.org/10.13952/j.cnki.jofmdr.2013.01.00510.13952/j.cnki.jofmdr.2013.01.005 ↗
37. Muratov KR, Ablyaz TR, D’yakonov AA, et al (2023) Improving hole geometry in honing. Russ Engin Res 43:1246–1248. https://doi.org/10.3103/S1068798X2310022210.3103/S1068798X23100222 ↗
38. 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 111:3111–3122. https://doi.org/10.1007/s00170-020-06321-710.1007/s00170-020-06321-7 ↗
39. Min H, Ning H, Gong J et al (2021) Motion simulation analysis and process practice in honing process. Mech Des and Manuf 306(4):248–252. https://doi.org/10.19356/j.cnki.1001-3997.2021.04.05710.19356/j.cnki.1001-3997.2021.04.057 ↗
40. He H, Yang C, Su H et al (2022) Research on prediction of honing material removal volume of aviation electro-hydraulic servo valve sleeve. Aeronaut Manuf Technol 65:87–94. https://doi.org/10.16080/j.issn1671-833x.2022.04.08710.16080/j.issn1671-833x.2022.04.087 ↗
41. Zhang X, Zhou Z, Yao Z et al (2020) Analytically predicating the multi-dimensional accuracy of the honed engine cylinder bore. J Tribol 142:091201. https://doi.org/10.1115/1.404667710.1115/1.4046677 ↗
42. Zhou Z, Zhang X, Yao Z et al (2017) Predicting multi-scale dimensional accuracy of engine cylinder by honing. In: ASME 2017 12th international manufacturing science and engineering conference collocated with the JSME/ASME 2017 6th international conference on materials and processing, 4–8 June, Los Angeles, California. https://doi.org/10.1115/MSEC2017-267310.1115/MSEC2017-2673 ↗
43. Zhang X, Wang X, Wang D et al (2017) Methodology to improve the cylindricity of engine cylinder bore by honing. J Manuf Sci Eng 139:031008. https://doi.org/10.1115/1.403462210.1115/1.4034622 ↗
44. Klein S, Fang S, Bähre D (2017) Analysis of different surface structures of hard metal guiding stones in the honing process. Procedia Manuf 10:265–275. https://doi.org/10.1016/j.promfg.2017.07.05510.1016/j.promfg.2017.07.055 ↗
45. Schmitt C, Bähre D (2014) Analysis of the process dynamics for the precision honing of bores. Procedia CIRP 17:692–697. https://doi.org/10.1016/j.procir.2014.01.05510.1016/j.procir.2014.01.055 ↗
46. 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. https://doi.org/10.1016/j.procir.2013.05.04810.1016/j.procir.2013.05.048 ↗