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Dressing principle and parameter optimization of ultrasonic-assisted diamond roller dressing WA grinding wheel using response surface methodology and genetic algorithm

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Abstract

To improve the surface quality of GCr15 bearing ring grinding and to address the problem of difficult selection of dressing parameters in the grinding wheel dressing process, a study was conducted on the optimal selection of parameters when dressing white alumina (WA) grinding wheel with ultrasonic-assisted diamond roller using response surface methodology (RSM) and genetic algorithm (GA). Firstly, we analyzed the action characteristics of the dressing parameters during ultrasonic-assisted roller dressing (UARD). Secondly, the Box–Behnken method was utilized to design the UARD experiments for the WA wheel. On the basis of experimental data, a prediction model for the surface roughness (Ra) of bearing ring grinding was developed. The influence law of dressing parameters (dressing speed ratio qd, crossfeed velocity vfd, dressing depth ad, ultrasonic amplitude AL) and their interactions on Ra was qualitatively analyzed using RSM. Moreover, it was found that the dressing speed ratio (qd) and the ultrasonic amplitude (AL) were the main influencing factors on Ra. According to the optimization model of dressing parameters established by the improved genetic algorithm (IGA), we have obtained the optimal combination of dressing parameters: qd = 0.4718, vfd = 50.001 mm/min, ad = 42 μm, and AL=1.29 μm. A surface roughness of Ra = 0.4175 m was attained by grinding GCr15 bearing ring with WA wheel dressed with the optimal dressing parameters. Its surface quality was significantly enhanced, and its surface roughness was reduced by 9.77~52.68% when compared to prior optimization. Finally, the results of the validation experiments show a certain level of accuracy and reliability in both the surface roughness prediction model and the dressing parameter optimization model. The study results show that the dressing parameter optimization method can effectively improve the grinding quality of bearing rings and is of value for engineering applications.

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References

  1. Abrāo AM, Aspinwall DK (1996) The surface integrity of turned and ground hardened bearing steel. Wear 196:279–284. https://doi.org/10.1016/0043-1648(96)06927-X

    Article  Google Scholar 

  2. Jouini N, Revel P, Thoquenne G (2020) Influence of surface integrity on fatigue life of bearing rings finished by precision hard turning and grinding. J Manuf Process 57:444–451. https://doi.org/10.1016/j.jmapro.2020.07.006

    Article  Google Scholar 

  3. Wegener K, Hoffmeister HW, Karpuschewski B et al (2011) Conditioning and monitoring of grinding wheels. Ann-Manuf Techn 60:757–777. https://doi.org/10.1016/j.cirp.2011.05.003

    Article  Google Scholar 

  4. Linke B, Klocke F (2010) Temperatures and wear mechanisms in dressing of vitrified bonded grinding wheels. Int J Mach Tool Manu 50:552–558. https://doi.org/10.1016/j.ijmachtools.2010.03.002

    Article  Google Scholar 

  5. Deng H, Xu Z (2019) Dressing methods of superabrasive grinding wheels: a review. J Manuf Process 45:46–69. https://doi.org/10.1016/j.jmapro.2019.06.020

    Article  Google Scholar 

  6. Deng H, Deng CH (2017) Progress on dressing technology of monolayer brazed diamond grinding wheel. Diamond Abrasives Eng 37:29–34. https://doi.org/10.13394/j.cnki.jgszz.2017.3.0007

    Article  Google Scholar 

  7. Baseri H, Rezaei SM, Rahimi A et al (2008) Analysis of the disc dressing effects on grinding performance—part 2: effects of the wheel topographical parameters on the specific energy and workpiece surface roughness. Mach Sci Technol 12:197–213. https://doi.org/10.1080/10910340802067429

    Article  CAS  Google Scholar 

  8. Kadivar M, Azarhoushang B, Shamray S et al (2018) The effect of dressing parameters on micro-grinding of titanium alloy. Precis Eng 51:176–185. https://doi.org/10.1016/j.precisioneng.2017.08.008

    Article  Google Scholar 

  9. Palmer J, Ghadbeigi H, Novovic D, Curtis D (2018) An experimental study of the effects of dressing parameters on the topography of grinding wheels during roller dressing. J Manuf Process 31:348–355. https://doi.org/10.1016/j.jmapro.2017.11.025

    Article  Google Scholar 

  10. Liu W, Deng ZH, Shang YY et al (2019) Parametric evaluation and three-dimensional modelling for surface topography of grinding wheel. Int J Mech Sci 155:334–342. https://doi.org/10.1016/j.ijmecsci.2019.03.006

    Article  Google Scholar 

  11. Garcia M, Alvarez J, Pombo I et al (2022) Investigation of the effects of speedratio and transversal overlapping ratio on CVD form roller dressing of corundum wheels and subsequent grinding performance. J Manuf Process 81:214–223. https://doi.org/10.1016/j.jmapro.2022.06.073

    Article  Google Scholar 

  12. Suresh PVS, Rao PV, Deshmukh SG (2002) A genetic algorithmic approach for optimization of surface roughness prediction model. Int J Mach Tool Manu 42:675–680. https://doi.org/10.1016/S0890-6955(02)00005-6

    Article  Google Scholar 

  13. Öztürk S, Kahraman MF (2019) Modeling and optimization of machining parameters during grinding of flat glass using response surface methodology and probabilistic uncertainty analysis based on Monte Carlo simulation. Measurement 145:274–291. https://doi.org/10.1016/j.measurement.2019.05.098

    Article  ADS  Google Scholar 

  14. Gao MY, Chen GY, Li W et al (2022) Optimization of laser sharpening parameters for diamond grinding wheel based on CNN. Diamond Abrasives Eng 42:602–609. https://doi.org/10.13394/j.cnki.jgszz.2022.0018

    Article  Google Scholar 

  15. Mukhopadhyay M, Kundu PK (2018) Optimization of dressing infeed of alumina wheel for grinding Ti-6Al-4V. Mater Manuf Process 33:1453–1458. https://doi.org/10.1080/10426914.2018.1453164

    Article  CAS  Google Scholar 

  16. Baseri H (2012) Simulated annealing based optimization of dressing conditions for increasing the grinding performance. Int J Adv Manuf Tech 59:531–538. https://doi.org/10.1007/s00170-011-3518-9

    Article  Google Scholar 

  17. Deng H, Chen GY, Zhou C et al (2014) Processing parameter optimization for the laser dressing of bronze-bonded diamond wheels. Appl Surf sci 290:475–481. https://doi.org/10.1016/j.apsusc.2013.11.120

    Article  ADS  CAS  Google Scholar 

  18. Aleksandrova I (2016) Optimization of the dressing parameters in cylindrical grinding based on a generalized utility function. Chin J Mech Eng-En 29:63–73. https://doi.org/10.3901/CJME.2015.1103.130

    Article  CAS  Google Scholar 

  19. Hung LX, Pi VN, Hong TT et al (2019) Multi-objective optimization of dressing parameters of internal cylindrical grinding for 9CrSi Aloy steel using taguchi method and grey relational analysis. Mater Today: Proc 18:2257–2264. https://doi.org/10.1016/j.matpr.2019.07.007

    Article  CAS  Google Scholar 

  20. Alok A, Das M (2019) Multi-objective optimization of cutting parameters during sustainable dry hard turning of AISI 52100 steel with newly develop HSN2-coated carbide insert. Measurement 133:288–302. https://doi.org/10.1016/j.measurement.2018.10.009

    Article  ADS  Google Scholar 

  21. Jia DZ, Li CH, Zhang YB et al (2019) Experimental evaluation of surface topographies of NMQL grinding ZrO2 ceramics combining multiangle ultrasonic vibration. Int J Adv Manuf Tech 100:457–473. https://doi.org/10.1007/s00170-018-2718-y

    Article  Google Scholar 

  22. Gao T, Zhang XP, Li CH et al (2020) Surface morphology evaluation of multi-angle 2D ultrasonic vibration integrated with nanofluid minimum quantity lubrication grinding. J Manuf Process 51:44–61. https://doi.org/10.1016/j.jmapro.2020.01.024

    Article  Google Scholar 

  23. Yang YY, Yang M, Li CH et al (2023) Machinability of ultrasonic vibration assisted micro-grinding in biological bone using nanolubricant. Front Mech Eng-Prc 18:1. https://doi.org/10.1007/s11465-022-0717-z

    Article  Google Scholar 

  24. Jiao F, Zhao B, Zhu XS et al (2006) Ultrasonic dressing of grinding wheel and its influence on grinding quality. Key Eng Mater 304-305:62–65. https://doi.org/10.4028/www.scientific.net/KEM.304-305.62

    Article  ADS  Google Scholar 

  25. Kitzig H, Tawakoli T, Azarhoushang B (2016) A novel ultrasonic-assisted dressing method of electroplated grinding wheels via stationary diamond dresser. Int J Adv Manuf Tech 86:487–494. https://doi.org/10.1007/s00170-015-8156-1

    Article  Google Scholar 

  26. Yang ZB, Zhang SY, Zhang Z et al (2019) Experimental research on laser-ultrasonic vibration synergic dressing of diamond wheel. J Mater Process Tech 269:182–189. https://doi.org/10.1016/j.jmatprotec.2019.01.031

    Article  Google Scholar 

  27. Li CL, Jiao F, Ma XS et al (2022) Modeling and optimization of longitudinal-torsional vibration horn with the large tool head. Appl Acoust 197:108902. https://doi.org/10.1016/j.apacoust.2022.108902

    Article  Google Scholar 

  28. Li CL, Jiao F, Ma XS et al (2023) Development of a longitudinal-torsional ultrasonic-assisted roller dressing device for precision form grinding of GCr15 bearing rings. Int J Adv Manuf Tech. https://doi.org/10.1007/s00170-023-11807-1

  29. Zhou X, Xi F (2002) Modeling and predicting surface roughness of the grinding process. Int J Mach Tool Manu 42:969–977. https://doi.org/10.1016/S0890-6955(02)00011-1

    Article  Google Scholar 

  30. Malkin S, Guo CS (2008) Grinding technology: theory and application of machining with abrasives. Industrial Press Inc

    Google Scholar 

  31. Wang NN, Zhang GP, Ren LJ et al (2022) Analysis of abrasive grain size effect of abrasive belt on material removal performance of GCr15 bearing steel. Tribol Int 171:107536. https://doi.org/10.1016/j.triboint.2022.107536

    Article  CAS  Google Scholar 

  32. Ferreira SC, Bruns RE, Ferreira HS et al (2007) Box-Behnken design: an alternative for the optimization of analytical methods. Anal Chim Acta 597:179–186. https://doi.org/10.1016/j.aca.2007.07.011

    Article  CAS  PubMed  Google Scholar 

  33. Wang ZK, Wang SW, Ding YY et al (2022) Process parameter modeling and optimization of abrasive water jet dressing fixed-abrasive pad based on Box–Behnken design. Materials 15:5251. https://doi.org/10.3390/ma15155251

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  34. Cui X, Li CH, Zhang YB et al (2022) Grindability of titanium alloy using cryogenic nanolubricant minimum quantity lubrication. J Manuf Process 80:273–286. https://doi.org/10.1016/j.jmapro.2022.06.003

    Article  Google Scholar 

  35. Cui ZM, Feng CC, Zhuang ZP et al (2021) Precision grinding technology of diamond abrasive tools based on grinding method. Diamond Abrasives Eng 41:5–11. https://doi.org/10.13394/j.cnki.jgszz.2021.3.0001

    Article  CAS  Google Scholar 

  36. Myers RH, Montgomery DC, Anderson-Cook CM (2016) Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons

    Google Scholar 

  37. Yang YQ, Li XY, Gu YX et al (2022) Adsorption property of fluoride in water by metal organic framework: optimization of the process by response surface methodology technique. Surf Interfaces 28:101649. https://doi.org/10.1016/j.surfin.2021.101649

    Article  CAS  Google Scholar 

  38. Sur G, Motorcu AR, Nohutçu S (2022) Single and multi-objective optimization for cutting force and surface roughness in peripheral milling of Ti6Al4V using fixed and variable helix angle tools. J Manuf Process 80:529–545. https://doi.org/10.1016/j.jmapro.2022.06.016

    Article  Google Scholar 

  39. Ghorbani J, Li J, Srivastava AK (2020) Application of optimized laser surface re-melting process on selective laser melted 316L stainless steel inclined parts. J Manuf Process 56:726–734. https://doi.org/10.1016/j.jmapro.2020.05.025

    Article  Google Scholar 

  40. Glantz SA, Slinker BK, Neilands TB (2001) Primer of applied regression & analysis of variance, vol 654. McGraw-Hill, Inc, New York

    Google Scholar 

  41. Daneshi A, Jandaghi N, Tawakoli T (2014) Effect of dressing on internal cylindrical grinding. Procedia CIRP 14:37–41. https://doi.org/10.1016/j.procir.2014.03.064

    Article  Google Scholar 

  42. Mohite DD, Jadhav VS, Nayak AN et al (2023) An influence of CNC grinding wheel dressing parameters on Ra value of EN19 steel. Mater Today: Proc. https://doi.org/10.1016/j.matpr.2023.02.260

  43. Dai J, Li YQ, Xiang DH (2022) The mechanism investigation of ultrasonic roller dressing vitrified bonded CBN grinding wheel. Ceram Int 48:24421–24430. https://doi.org/10.1016/j.ceramint.2022.05.049

    Article  CAS  Google Scholar 

  44. Kumabe J, Fuchizawa K, Soutome T, Nishimoto Y (1989) Ultrasonic superposition vibration cutting of ceramics. Precis Eng 11:71–77. https://doi.org/10.1016/0141-6359(89)90055-X

    Article  Google Scholar 

  45. Ma CX, Shamoto E, Moriwaki T, Wang LJ (2004) Study of machining accuracy in ultrasonic elliptical vibration cutting. Int J Mach Tool Manu 44(12-13):1305–1310. https://doi.org/10.1016/j.ijmachtools.2004.04.014

    Article  Google Scholar 

  46. Gen M, Cheng R (1999) Genetic algorithms and engineering optimization, vol 7. John Wiley & Sons

    Book  Google Scholar 

  47. Zhou WH, Tang JY, Chen HF et al (2018) A comprehensive investigation of plowing and grain-workpiece micro interactions on 3D ground surface topography. Int J Mech Sci 144:639–653. https://doi.org/10.1016/j.ijmecsci.2018.06.024

    Article  Google Scholar 

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Funding

This research was supported by the National Natural Science Foundation of China (grant number 52175399), the Key Research & Development and Promotion Program of Henan Province (grant number 212102210056), and the Fundamental Research Funds for the Universities of Henan Province (grant number NSFRF200102).

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Authors and Affiliations

  1. School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454003, China

    Chenglong Li, Feng Jiao, Xiaosan Ma, Ying Niu & Jinglin Tong

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  1. Chenglong Li
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  2. Feng Jiao
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  3. Xiaosan Ma
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Contributions

Chenglong Li: methodology, investigation, formal analysis, writing—review and editing, and software. Feng Jiao: writing—review and editing, conceptualization, supervision, funding acquisition, and project administration. Xiaosan Ma: supervision and data curation. Ying Niu: validation, formal analysis, and data curation. Jinglin Tong: resources, supervision, and visualization.

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Correspondence to Feng Jiao.

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Li, C., Jiao, F., Ma, X. et al. Dressing principle and parameter optimization of ultrasonic-assisted diamond roller dressing WA grinding wheel using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 131, 2551–2568 (2024). https://doi.org/10.1007/s00170-023-11916-x

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