Sensitivity analysis of near solidus forming (NSF) process with digital twin using Taguchi approach

Muhammad Sajjad , Javier Trinidad , Gorka Plata , Jokin Lozares , Joseba Mendiguren

Advances in Manufacturing ›› 2024, Vol. 13 ›› Issue (2) : 322 -336.

PDF
Advances in Manufacturing ›› 2024, Vol. 13 ›› Issue (2) : 322 -336. DOI: 10.1007/s40436-024-00482-4
Article

Sensitivity analysis of near solidus forming (NSF) process with digital twin using Taguchi approach

Author information +
History +
PDF

Abstract

Forging at near solidus material state takes advantage of the high ductility of the material at the semi solid or soft-solid state while keeping most of the mechanical properties of a forged part. The technology is at maturity level ready for its industrial implementation. However, to implement the process for complex cases the development of an appropriate digital twin (DT) is necessary. While developing a material model, a strong experimental and DT is necessary to be able to evaluate the accuracy of the model. Aimed at having a reliable DT under control, for future material model validations, the main objective of this work is to develop a sensitivity analysis of three NSF industrial cases such as Hook, R spindle and H spindle to develop an adequate DT calibration procedure. Firstly, the benchmark experimentation process parameter noise and experimentation boundary conditions (BCs) parameter uncertainty are identified. Secondly, the three industrial benchmark DTs are constructed, and a Taguchi design of experiments (DoEs) methodology is put in place to develop the sensitivity analysis. Finally, after simulations the results are critically evaluated and the sensitivity of each benchmark to the different inputs (process parameter noise and BC parameter uncertainty) is studied. Lastly, the optimum DT calibration procedure is developed. Overall, the results stated the minimum impact of the material model in terms of dies filling. Nevertheless, even if the material model is the highest impacting factor for the forging forces other inputs, such as heat transfer and friction must be under control first.

Keywords

Near solidus / Digital twin (DT) / Taguchi design / Sensitivity analysis / Heat transfer

Cite this article

Download citation ▾
Muhammad Sajjad, Javier Trinidad, Gorka Plata, Jokin Lozares, Joseba Mendiguren. Sensitivity analysis of near solidus forming (NSF) process with digital twin using Taguchi approach. Advances in Manufacturing, 2024, 13(2): 322-336 DOI:10.1007/s40436-024-00482-4

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

DucatoA, BuffaG, FratiniL, et al. . Dual phase titanium alloy hot forging process design: experiments and numerical modeling. Adv Manuf, 2015, 3: 269-281.

[2]

TuranE, KonuşkanY, YıldırımN, et al. . Digital twin modelling for optimizing the material consumption: a case study on sustainability improvement of thermoforming process. Sustain Comput Inform Syst, 2022, 35: 100655.

[3]

MuraliS, YongMS. Liquid forging of thin Al-Si structures. J Mater Process Technol, 2010, 210: 1276-1281.

[4]

BayramogluM, PolatH, GerenN. Cost and performance evaluation of different surface treated dies for hot forging process. J Mater Process Technol, 2008, 205: 394-403.

[5]

KirkwoodDH. Semisolid metal processing. Int Mater Rev, 1994, 39: 173-189.

[6]

FanZ. Semisolid metal processing. Int Mater Rev, 2002, 47: 49-86.

[7]

LiuK, ChenXG. Influence of the modification of iron-bearing intermetallic and eutectic Si on the mechanical behavior near the solidus temperature in Al-Si-Cu 319 cast alloy. Physica B Condens Matter, 2019, 560: 126-132.

[8]

LozaresJ, PlataG, HurtadoI, et al. . Near solidus forming (NSF): semi-solid steel forming at high solid content to obtain as-forged properties. Metals (Basel), 2020, 102198.

[9]

PlataG, LozaresJ, SánchezA, et al. . Preliminary study on the capability of the novel near solidus forming (NSF) technology to manufacture complex steel components. Materials, 2020, 13204682.

[10]

RogalDJ, AtkinsonHV, et al. . Characterization of semi-solid processing of aluminium alloy 7075 with Sc and Zr additions. Mater Sci Eng A, 2013, 580: 362-373.

[11]

PsarommatisF, MayG. A literature review and design methodology for digital twins in the era of zero defect manufacturing. Int J Prod Res, 2023, 61: 5723-5743.

[12]

ScaglioniB, FerrettiG. Towards digital twins through object-oriented modelling: a machine tool case study. IFAC-PapersOnLine, 2018, 51: 613-618.

[13]

HürkampA, LorenzR, OssowskiT, et al. . Simulation-based digital twin for the manufacturing of thermoplastic composites. Procedia CIRP, 2021, 100: 1-6.

[14]

KnustJ, PodszusF, StonisM, et al. . Preform optimization for hot forging processes using genetic algorithms. Int J Adv Manuf Technol, 2017, 89: 1623-1634.

[15]

SołekKP, Łukaszek-SołekA, KuziakR. Rheological properties of alloys near solidus point intended for thixoforming. Arch Civ Mech Eng, 2009, 9: 111-117.

[16]

Hopmann C, Klein J, Schöngart M (2016) Determination of the strain rate dependent thermal softening behavior of thermoplastic materials for crash simulations. In: AIP conference proceedings. American Institute of Physics Inc, South Korea

[17]

MonajatiH, JahaziM, YueS, et al. . Deformation characteristics of isothermally forged UDIMET 720 Nickel-base superalloy. Metall Mater Trans A, 2005, 36: 895-905.

[18]

SubrotoT, MirouxA, EskinDG, et al. . Tensile mechanical properties, constitutive parameters and fracture characteristics of an as-cast AA7050 alloy in the near-solidus temperature regime. Mater Sci Eng, A, 2017, 679: 28-35.

[19]

MalinowskiZ, LenardJG, DaviesME. A study of the heat-transfer coefficient as a function of temperature and pressure. J Mater Process Technol, 1994, 41: 125-142.

[20]

BurtePR, ImYT, AltanT, et al. . Measurement and analysis of heat transfer and friction during hot forging. J Manuf Sci Eng, 1990, 112: 332-339

[21]

Sethy R, Galdos L, Mendiguren J et al (2016) Investigation of influencing factors on friction during ring test in hot forging using FEM simulation. In: AIP conference proceedings. American Institute of Physics Inc, Nantes

[22]

BeckerE, FavierV, BigotR, et al. . Impact of experimental conditions on material response during forming of steel in semi-solid state. J Mater Process Technol, 2010, 210: 1482-1492.

[23]

Barrau O, Boher C, Vergne C et al (2002) Investigations of friction and wear mechanisms of hot forging tool steels. In: 6th International tooling conference, vol 2001, pp 95–111

[24]

Bogdan O (2012) Influence of ingot size and mold design on macro-segregation in AISI 4340 forging ingots. In: Proceedings of the 1st international conference on ingot casting, rolling and forging, Aachen, Germany

[25]

Traidi K, Favier V, Lestriez P et al (2016) Thermomechanical steels behaviors at semi-solid state. In: AIP conference proceedings. American Institute of Physics Inc, Nantes

[26]

Andrade-CamposA, Teixeira-DiasF, KruppU, et al. . Effect of strain rate, adiabatic heating and phase transformation phenomena on the mechanical behaviour of stainless steel. Strain, 2010, 46: 283-297.

[27]

TirthV, ArabiA. Effect of liquid forging pressure on solubility and freezing coefficients of cast aluminum 2124, 2218 and 6063 alloys. Arch Metall Mater, 2020, 65: 357-366.

[28]

KocM, VazquezV, WitulskiT, et al. . Materials processing technology application of the finite element method to predict material flow and defects in the semi-solid forging of A356 aluminum alloys. J Mater Process Technol, 1996, 59: 106-112.

[29]

Mills KC (2005) Measurement and estimation of physical properties of metals at high temperatures. In: Fundamentals of metallurgy. Elsevier, pp 109–177

[30]

HeB, BaiK-J. Digital twin-based sustainable intelligent manufacturing: a review. Adv Manuf, 2021, 9: 1-21.

[31]

SlaterC, PlataG, SánchezA, et al. . A novel forming technique to coforge bimetal components into complex geometries. Manuf Lett, 2020, 26: 21-24

[32]

ZhangDW, LiSP, JingF, et al. . Initial position optimization of preform for large-scale strut forging. Int J Adv Manuf Technol, 2018, 94: 2803-2810.

[33]

Murillo-MarrodanA, GarciaE, CortesF. A study of friction model performance in a skew rolling process numerical simulation. Int J Simul Modell, 2018, 17: 569-582.

[34]

RosaJL, RobinA, SilvaMB, et al. . Electrodeposition of copper on titanium wires: Taguchi experimental design approach. J Mater Process Technol, 2009, 209: 1181-1188.

[35]

Zhang J, Wu D, Zhou J et al (2014) Multi-objective optimization of process parameters for 7050 aluminum alloy rib-web forgings’ precise forming based on Taguchi method. In: Procedia engineering. Elsevier Ltd, Nagoya, pp 558–563

[36]

EqubalMI, KumarR, ShamimM, et al. . A grey-based Taguchi method to optimize hot forging process. Procedia Mater Sci, 2014, 6: 1495-1504.

[37]

HallstromJ. Influence of friction on die filling in counterblow hammer forging. J Mater Process Technol, 2000, 108: 21-25.

[38]

HawrylukM, JakubikJ. Analysis of forging defects for selected industrial die forging processes. Eng Fail Anal, 2016, 59: 396-409.

[39]

VazquezV, AltanT. Die design for flashless forging of complex parts. J Mater Process Technol, 2000, 98: 81-89.

[40]

MendigurenJ, OrtubayR, De ArgandonãES, et al. . Experimental characterization of the heat transfer coefficient under different close loop controlled pressures and die temperatures. Appl Therm Eng, 2016, 99: 813-824.

Funding

Research Fund for Coal and Steel(800763)

Mondragon Unibertsitatea

RIGHTS & PERMISSIONS

The Author(s)

AI Summary AI Mindmap
PDF

173

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/