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Development of surrogate models of clamp configuration for optical glass lens centering through finite element analysis and machine learning

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Abstract

In this study, the clamping stress and force involved in the centering of optical glass lens were evaluated and quantified. On the basis of the key design parameters of the examined clamps, the finite element method was applied to predict clamping stress under various parameter combinations. Support vector regression, Gaussian process regression, and adaptive neuro fuzzy inference system algorithm of surrogate models were established using the results obtained through finite element simulation. These surrogate models, which can predict clamping stress on the basis of key parameters, can reduce the time required to perform finite element analysis while providing references for optimizing clamp configuration.

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References

  1. Latyev SM, Rumyantsev DM, Kuritsyn PA (2013) Design and process methods of centering lens systems. J Opt Technol 80(3):197–200

    Article  Google Scholar 

  2. Beier M, Gebhardt A, Eberhardt R, Tünnermann A (2012) Lens centering of aspheres for high-quality optics. THOSS Media & DE GRUYTER, Adv Opt Technol 1(6):441–446

    Google Scholar 

  3. Liu CW, Shiu SC, Yu KH (2022) Analysis of the optical quartz lens centering process based on acoustic emission signal processing and the support vector machine. Int J Adv Manuf Technol

  4. Dengkui F, Wenfeng D, Qing M, Jiuhua X (2017) Simulation research on the grinding forces and stresses distribution in single-grain surface grinding of Ti-6Al-4V alloy when considering the actual cutting-depth variation. Int J Adv Manuf Technol 91:9–12

  5. Javed MF, Ramli NH, Kashif-ur-Rehman S, Khan NB (2017) Finite element analysis on the structural behaviour of square CFST beams, IOP Conference Series Materials Science and Engineering 210(1):012018, 5–6 April 2017. University of Malaya, Kuala Lumpur, Malaysia

    Google Scholar 

  6. Ma Q, Lin Z, Yu Z (2009) Prediction of deformation behavior and microstructure evolution in heavy forging by FEM. Int J Adv Manuf Technol 40:253–260

    Article  Google Scholar 

  7. Tuominen V (2011) Virtual clamping in automotive production line measurement. Expert Syst Appl 38:15065–15074

    Article  Google Scholar 

  8. Olshevskiy A, Yang HI, Kim CW (2011) Finite element simulation of inelastic contact for arbitrarily shaped rough bodies. ARCHIVE Proc Inst Mech Eng Part C J Mechanical Eng Sci 1989–1996 (vols 203–210) 226(3):595–606

  9. Liu Z, Kang R, Liu H, Dong Z, Bao Y, Gao S, Zhu X (2020) FEM-based optimization approach to machining strategy for thin-walled parts made of hard and brittle materials. Int J Adv Manuf Technol 110:1399–1413

    Article  Google Scholar 

  10. Lee KS, Lin JC (2006) Design of the runner and gating system parameters for a multi-cavity injection mould using FEM and neural network. Int J Adv Manuf Technol 27:1089–1096

    Article  Google Scholar 

  11. Ryser M, Neuhauser FM, Hein C, Hora P, Bambach M (2021) Surrogate model–based inverse parameter estimation in deep drawing using automatic knowledge acquisition. Int J Adv Manuf Technol 117:997–1013

    Article  Google Scholar 

  12. Hürkamp A, Gellrich S, Dér A, Herrmann C, Dröder K, Thiede S (2021) Machine learning and simulation-based surrogate modelling for improved process chain operation. Int J Adv Manuf Technol 117:2297–2307

    Article  Google Scholar 

  13. António CC, Rasheed S (2018) A displacement field approach based on FEM-ANN and experiments for identification of elastic properties of composites. Int J Adv Manuf Technol 95:4279–4291

    Article  Google Scholar 

  14. Tapia G, Khairallah S, Matthews M, King WE, Elwany A (2018) Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel. Int J Adv Manuf Technol 94:3591–3603

    Article  Google Scholar 

  15. Jun Z, Youqiang Z, Wei C, Fu C (2021) Research on prediction of contact stress of acetabular lining based on principal component analysis and support vector regression. Biotechnol Biotechnol Equip 35(1):462–468

  16. Huang T, Song X, Liu M (2018) The multi-objective non-probabilistic interval optimization of the loading paths for T-shape tube hydroforming. Int J Adv Manuf Technol 94:677–686

    Article  Google Scholar 

  17. Fan Y, Lu W, Miao T, An Y, Li J, Luo J (2020) Optimal design of groundwater pollution monitoring network based on the SVR surrogate model under uncertainty. Environ Sci Pollut Res 27:24090–24102

    Article  Google Scholar 

  18. Xiang H, Li Y, Liao H, Li C (2017) An adaptive surrogate model based on support vector regression and its application to the optimization of railway wind barriers. Struct Multidisc Optim 55:701–713

    Article  Google Scholar 

  19. Liu Y, Chen W, Ding L, Wang X (2013) Response surface methodology based on support vector regression for polygon blank shape optimization design. Int J Adv Manuf Technol 66:1397–1405

    Article  Google Scholar 

  20. Rafiee V, Faiz J (2019) Robust design of an outer rotor permanent magnet motor through six-sigma methodology using response surface surrogate model, IEEE Trans Magnetics 55(10)

  21. Naceur H, Ben-Elechi S, Batoz JL, Knopf-Lenoir C (2008) Response surface methodology for the rapid design of aluminium sheet metal forming parameters. Mater Des 29(4):781–790

    Article  Google Scholar 

  22. Jiang P, Cao L, Zhou Q, Gao Z, Rong Y, Shao X (2016) Optimization of welding process parameters by combining Kriging surrogate with particle swarm optimization algorithm. Int J Adv Manuf Technol 86:2473–2483

    Article  Google Scholar 

  23. Ma X, Zhang Z, Hua H (2022) Uncertainty quantization and reliability analysis for rotor/stator rub-impact using advanced Kriging surrogate model. J Sound Vib 525(12):116800

    Article  Google Scholar 

  24. Salonitis K, Kolios A (2014) Reliability assessment of cutting tool life based on surrogate approximation methods. Int J Adv Manuf Technol 71:1197–1208

    Article  Google Scholar 

  25. Santos LF, Costa CBB, Caballero JA, Ravagnani MASS (2022) Framework for embedding black-box simulation into mathematical programming via kriging surrogate model applied to natural gas liquefaction process optimization. Appl Energy 310(15):118537

    Article  Google Scholar 

  26. Mohajernia B, Mirazimzadeh SE, Pasha A, Urbanic RJ (2022) Machine learning approaches for predicting geometric and mechanical characteristics for single P420 laser beads clad onto an AISI 1018 substrate. Int J Adv Manuf Technol 118:3691–3710

    Article  Google Scholar 

  27. Hamedi M (2005) Intelligent fixture design through a hybrid system of artificial neural network and genetic algorithm. Artif Intell Rev 2005(23):295–311

    Article  Google Scholar 

  28. Selvakumar S, Arulshri KP, Padmanaban KP, Sasikumar KSK (2013) Design and optimization of machining fixture layout using ANN and DOE. Int J Adv Manuf Technol 2013(65):1573–1586

    Article  Google Scholar 

  29. Marinescu ID, Rowe WB, Dimitrov B, Inasaki I (2004) Tribology of abrasive machining processes. William Andrew Inc, Norwich, NY

    Google Scholar 

  30. Gostimirović M, Rodić D, Kovač P, Jesić D, Kulundžic N (2015) Investigation of the cutting forces in creep-feed surface grinding process. J Prod Eng 18(2)

  31. Lu J, Zhang Z, Yuan X, Ma J, Hu S, Xue B, Liao X (2020) Effect of machining parameters on surface roughness for compacted graphite cast iron by analyzing covariance function of Gaussian process regression. Measurement 157:107578

    Article  Google Scholar 

  32. Jajarmi E, Sajjadi SA, Mohebbi J (2019) Predicting the relative density and hardness of 3YPSZ/316L composites using adaptive neuro-fuzzy inference system and support vector. Measurement 145:472–479

    Article  Google Scholar 

  33. Hourmand M, Sarhan AAD, Farahany S, Sayuti M (2019) Microstructure characterization and maximization of the material removal rate in nano-powder mixed EDM of Al-Mg2Si metal matrix composite—ANFIS and RSM approaches. Int J Adv Manuf Technol 101:2723–2737

    Article  Google Scholar 

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Funding

The authors are grateful for the support of the Research Project of the Ministry of Science and Technology, Taiwan (MOST 110–2222-E-007 -009 -MY3 & MOST 110–2218-E-007 -053).

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Authors and Affiliations

  1. National Tsing Hua University, Power Mechanical Engineering, Section 2, Guangfu Road, Hsinchu, 300044, Taiwan

    Kai-Hung Yu, Shiau-Cheng Shiu & Chun-Wei Liu

Authors
  1. Kai-Hung Yu
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  2. Shiau-Cheng Shiu
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  3. Chun-Wei Liu
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Contributions

Chun-Wei Liu: Conceptualization, supervision Shiau-Cheng Shiu: Finite element simulation, convergence analysis Kai-Hung Yu: Experiment design, writing, algorithmic modeling.

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Correspondence to Chun-Wei Liu.

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Yu, KH., Shiu, SC. & Liu, CW. Development of surrogate models of clamp configuration for optical glass lens centering through finite element analysis and machine learning. Int J Adv Manuf Technol 121, 8209–8220 (2022). https://doi.org/10.1007/s00170-022-09915-5

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  • DOI: https://doi.org/10.1007/s00170-022-09915-5

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