Process-Structure-Property Modeling for Severe Plastic Deformation Processes Using Orientation Imaging Microscopy and Data-Driven Techniques
Tóm tắt
Machining is a severe plastic deformation process, wherein the workpiece material is subjected to high deformation rates and temperatures. During metal machining, the dynamic recrystallization mechanism causes grain refinement into the sub-micron range. In this study, we investigate the microstructure evolution of oxygen-free high conductivity copper (OFHC Cu) subject to a machining process where the cutting speed and rake angle are controlled to manipulate the process strain, strain rate, and temperatures. Microstructures of the deformed chips are quantified using orientation imaging microscopy and novel statistical descriptors that capture the morphology and local lattice misorientations generated during the several mechanistic stages of the dynamic recrystallization process. Mechanical properties of the resulting chips are quantified using spherical nanoindentation protocols. A multiple output Gaussian process regression model is used to simultaneously model the structure-property evolution, which differs from more common approaches that establish such relationships sequentially. This modeling strategy is particularly attractive since it can flexibly provide both structure and property uncertainty estimates. In addition, the statistical modeling framework allows for the inclusion of multi-fidelity data. The statistical metrics utilized serve as efficient microstructure descriptors, which retain the physics of the observed structures without having to introduce ad hoc microstructure feature definitions.
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Tài liệu tham khảo
Shaw MC, Cookson J (1984) Metal cutting principles. Clarendon Press, Oxford
Sagapuram D, Yeung H, Guo Y, Mahato A, M’Saoubi R, Compton WD, Trumble KP, Chandrasekar S (2015) On control of flow instabilities in cutting of metals. CIRP Annals 64(1):49
Brown TL, Saldana C, Murthy TG, Mann JB, Guo Y, Allard LF, King AH, Compton WD, Trumble KP, Chandrasekar S (2009) A study of the interactive effects of strain, strain rate and temperature in severe plastic deformation of copper. Acta Materialia 57(18):5491–5500
Basu S, Shankar MR (2015) Crystallographic textures resulting from severe shear deformation in machining. Metall Mater Trans A 46(2):801–812
Wang Z, Basu S, Murthy TG, Saldana C (2018) Gradient microstructure and texture in wedge-based severe plastic burnishing of copper. J Mater Res 33(8):1046
Wang Z, Basu S, Saldana C (2017) Low-temperature machining in a fully submerged cryogenic environment. Mach Sci Technol 21(1):19
Guo Y, Saldana C, Compton WD, Chandrasekar S (2011) Controlling deformation and microstructure on machined surfaces. Acta materialia 59(11):4538–4547
M’Saoubi R, Larsson T, Outeiro J, Guo Y, Suslov S, Saldana C, Chandrasekar S (2012) Surface integrity analysis of machined inconel 718 over multiple length scales. CIRP Ann-Manuf Technol 61(1):99–102
Ni H, Elmadagli M, Alpas A (2004) Mechanical properties and microstructures of 1100 aluminum subjected to dry machining. Mater Sci Eng A 385(1-2):267–278
Swaminathan S, Shankar M, Lee S, Hwang J, King AH, Kezar RF, Rao BC, Brown TL, Chandrasekar S, Compton WD et al (2005) Large strain deformation and ultra-fine grained materials by machining. Mater Sci Eng A 410:358
Valiev RZ, Islamgaliev RK, Alexandrov IV (2000) Bulk nanostructured materials from severe plastic deformation. Progress Mater Sci 45(2):103–189
Zhilyaev AP, Langdon TG (2008) Using high-pressure torsion for metal processing: Fun- damentals and applications. Prog Mater Sci 53(6):893–979
Murr L, Ramirez A, Gaytan S, Lopez M, Martinez E, Hernandez D, Martinez E (2009) Microstructure evolution associated with adiabatic shear bands and shear band failure in ballistic plug formation in ti-6al-4v targets. Mater Sci Eng A 516(1–2):205–216
Minnaar K, Zhou M (1998) An analysis of the dynamic shear failure resistance of structural metals. J Mech Phys Solids 46(10):2155–2170
Me-Bar Y, Shechtman D (1983) On the adiabatic shear of ti 6al 4v ballistic targets. Mater Sci Eng 58 (2):181–188
Fatemi-Varzaneh S, Zarei-Hanzaki A, Beladi H (2007) Dynamic recrystallization in az31 magnesium alloy. Mater Sci Eng A 456(1–2):52–57
Ion S, Humphreys F, White S (1982) Dynamic recrystallisation and the development of microstructure during the high temperature deformation of magnesium. Acta Metall 30(10):1909–1919
Tóth L, Beausir B, Gu C, Estrin Y, Scheerbaum N, Davies C (2010) Effect of grain refinement by severe plastic deformation on the next-neighbor misorientation distribution. Acta Mater 58(20):6706–6716
Abolghasem S, Basu S, Shekhar S, Cai J, Shankar M (2012) Mapping subgrain sizes resulting from severe simple shear deformation. Acta Mater 60(1):376–386
Shekhar S, Abolghasem S, Basu S, Cai J, Shankar M (2012) Effect of severe plastic deformation in machining elucidated via rate-strain-microstructure mappings. J Manuf Sci Eng 134(3):031008
Basu S, Wang Z, Liu R, Saldana C (2016) Enhanced subsurface grain refinement during transient shear-based surface generation. Acta Mater 116:114–123
Kalidindi SR, Medford AJ, McDowell DL (2016) Vision for data and informatics in the future materials innovation ecosystem. JOM 68(8):2126–2137
Kalidindi SR, Brough DB, Li S, Cecen A, Blekh AL, Congo FYP, Campbell C (2016) Role of materials data science and informatics in accelerated materials innovation. Mrs Bull 41(8):596–602
Kalidindi SR (2015) Hierarchical materials informatics: novel analytics for materials data. Elsevier
Niezgoda SR (2010) Stochastic representation of microstructure via higher-order statistics: theory and application
Paulson NH, Priddy MW, McDowell DL, Kalidindi SR (2017) Reduced-order structure property linkages for polycrystalline microstructures based on 2-point statistics. Acta Mater 129:428–438
Paulson NH, Priddy MW, McDowell DL, Kalidindi SR (2018) Materials & Design
Yang Z, Li X, Brinson LC, Choudhary AN, Chen W, Agrawal A (2018) arXiv:1805.02791
Pathak S, Shaffer J, Kalidindi SR (2009) Determination of an effective zero-point and extraction of indentation stress-strain curves without the continuous stiffness measurement signal. Scr Mater 60(6):439–442
Pathak S, Stojakovic D, Doherty R, Kalidindi S (2009) Importance of surface preparation on the nano-indentation stress-strain curves measured in metals. J Mater Res 24(3):1142–1155
Bunge HJ (2013) Texture analysis in materials science: mathematical methods. Elsevier
Yabansu YC, Patel DK, Kalidindi S (2014) Calibrated localization relationships for elastic response of polycrystalline aggregates. Acta Mater 81:151–160
Adams BL, Gao XC, Kalidindi S (2005) Finite approximations to the second-order properties closure in single phase polycrystals. Acta Mater 53(13):3563–3577
Torquato S (2013) Random heterogeneous materials: microstructure and macroscopic properties, vol 16. Springer Science & Business Media
Fast T, Niezgoda SR, Kalidindi S (2011) A new framework for computationally efficient structure-structure evolution linkages to facilitate high-fidelity scale bridging in multiscale materials models. Acta Mater 59(2):699–707
Cecen A, Fast T, Kalidindi SR (2016) Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure. Integr Mater Manuf Innov 5(1):1
Cecen A, Yabansu YC, Kalidindi SR (2018) A new framework for rotationally invariant two-point spatial correlations in microstructure datasets. Acta Materialia
Cecen A, Fast T, Kumbur E, Kalidindi S (2014) A data-driven approach to establishing microstructure-property relationships in porous transport layers of polymer electrolyte fuel cells. J Power Sources 245:144–153
Iskakov A, Yabansu YC, Rajagopalan S, Kapustina A, Kalidindi S (2018) Application of spherical indentation and the materials knowledge system framework to establishing microstructure-yield strength linkages from carbon steel scoops excised from high-temperature exposed components. Acta Mater 144:758–767
Khosravani A, Cecen A, Kalidindi SR (2017) Development of high throughput assays for establishing process-structure-property linkages in multiphase polycrystalline metals: Application to dual-phase steels. Acta Mater 123:55–69
Sundararaghavan V, Zabaras N (2004) A dynamic material library for the representation of single-phase polyhedral microstructures. Acta Materialia 52(14):4111–4119
Sundararaghavan V, Zabaras N (2005) Classification and reconstruction of three-dimensional microstructures using support vector machines. Comput Mater Sci 32(2):223–239
Wargo E, Hanna A, Cecen A, Kalidindi S, Kumbur E (2012) Selection of representative volume elements for pore-scale analysis of transport in fuel cell materials. J Power Sources 197:168–179
Cecen A, Wargo E, Hanna A, Turner D, Kalidindi S, Kumbur E (2012) 3-d microstructure analysis of fuel cell materials: spatial distributions of tortuosity, void size and diffusivity. J Electrochem Soc 159 (3):B299–B307
Niezgoda SR, Yabansu YC, Kalidindi S (2011) Understanding and visualizing microstructure and microstructure variance as a stochastic process. Acta Mater 59(16):6387–6400
Deshpande P, Gautham B, Cecen A, Kalidindi S, Agrawal A, Choudhary A (2013) Application of statistical and machine learning techniques for correlating properties to composition and manufacturing processes of steels. In: Proceedings of the 2nd World congress on integrated computational materials engineering (ICME). Springer, pp 155–160
Rasmussen CE (2004) In: Advanced lectures on machine learning. Springer, pp 63–71
Santner TJ, Williams BJ, Notz WI (2013) The design and analysis of computer experiments. Springer Science & Business Media
Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, Berlin
Pan SJ, Yang Q, et al. (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359
Haaland B, Qian PZ (2010) An approach to constructing nested space-filling designs for multi-fidelity computer experiments. Stat Sin 20(3):1063
Tuo R, Wu CJ, Yu D (2014) Surrogate modeling of computer experiments with different mesh densities. Technometrics 56(3):372–380
Kennedy MC, O’Hagan A (2001) Bayesian calibration of computer models. J R Stat Soc Ser B (Stat Methodol) 63(3):425–464
Shaw MC, Cookson J (2005) Metal cutting principles, vol 2. Oxford University Press, New York
Agrawal A, Deshpande PD, Cecen A, Basavarsu GP, Choudhary AN, Kalidindi SR (2014) Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters. Integr Mater Manuf Innov 3(1):8
Pilania G, Mannodi-Kanakkithodi A, Uberuaga B, Ramprasad R, Gubernatis J, Lookman T (2016) Machine learning bandgaps of double perovskites. Sci Rep 6:19375
Fuentes M (2001) A high frequency kriging approach for non-stationary environmental processes. Environmetrics: Off J Int Environmetr Soc 12(5):469–483
Boyle P, Frean M (2005) Dependent gaussian processes. In: Advances in neural information processing systems, pp 217–224
Fernandez-Zelaia P, Melkote SN (2018) Statistical calibration and uncertainty quantification of complex machining computer models. International Journal of Machine Tools and Manufacture
Fernandez-Zelaia P (2019) Machining psp. https://github.com/pfz3
Fernandez-Zelaia P, Joseph VR, Kalidindi S, Melkote SN (2018) Estimating mechanical properties from spherical indentation using bayesian approaches. Mater Des 147:92–105
Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, Brubaker M, Guo J, Li P, Riddell A (2017) Stan: A probabilistic programming language. J Statist Softw, 76(1)
Stan Development Team (2018) RStan: the R interface to Stan. http://mc-stan.org/. R package version 2.17.3