Surrogate-Assisted Parametric Calibration Using Design of Experiment Platform Within Digital Twinning

Surrogate-Assisted Parametric Calibration Using Design of Experiment Platform Within Digital Twinning

Madhu Sudan Sapkota Edward Apeh Mark Hadfield Roya Haratian Robert Adey John Baynham

Faculty of Science and Technology, Bournemouth University Poole, United Kingdom

CM BEASY Ltd, Ashurst Lodge, UK

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© 2022 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (



The process of developing a virtual replica of a physical asset usually involves using the best available values of the material and environment-related parameters essential to run the predictive simulation. The parameter values are further updated as necessary over time in response to the behaviour/condi- tions of physical assets and/or environment. This parametric calibration of the simulation models is usually made manually with trial-and-error using data obtained from sensors/manual survey readings of designated parts of the physical asset. Digital twining (DT) has provided a means by which validating data from the physical asset can be obtained in near real time. However, the process of calibration is time-consuming as it is manual, and as with each parameter guess during the trial, a simulation run is required. This is even more so when the running time of a single simulation is high enough, like hours or even days, and the model involves a significantly high number of parameters. To address these shortcomings, an experimental platform implemented with the integration of a simulator and scientific software is proposed. The scientific software within the platform also offers surrogate building support, where surrogates assist in the estimation/update of design parameters as an alternative to time-consum- ing predictive models. The proposed platform is demonstrated using BEASY, a simulator designed to predict protection provided by a cathodic protection (CP) system to an asset, with MATLAB as the scientific software. The developed setup facilitates the task of model validation and adaptation of the CP model by automating the process within a DT ecosystem and also offers surrogate-assisted optimisation for parameter estimation/updating.


BEASY, cathodic-protection, digital twin, model adaptation, software integration


[1] ASTM, .Standard test method for strain-controlled fatigue testing. E606/E606M-12,96(2004), pp. 1–16, 2004.

[2] S. Cheruvathur, E. A. Lass, and C. E. Campbell, Additive manufacturing of 17-4 PHstainless steel: post-processing heat treatment to achieve uniform reproducible microstructure.JOM, 68, pp. 930–942, 2016.

[3] S. steel A. 17-4TM precipitation ATI technical data sheet and H. Alloy, Stainless steelAL 17-4 TM precipitation hardening alloy. Allegheny Technologies, 2006.

[4] C. N. Hsiao, C. S. Chiou, and J. R. Yang, Aging reactions in a 17-4 PH stainless steel. MaterChem Phys, 74(2), pp. 134–142, 2002.

[5] H. K. Rafi, D. Pal, N. Patil, T. L. Starr, and B. E. Stucker, Microstructure and mechanicalbehavior of 17-4 precipitation hardenable steel processed by selective laser melting.J Mater Eng Perform, 23(12), pp. 4421–4428, 2014.

[6] U. K. Viswanathan, S. Banerjee, and R. Krishnan, Effects of aging on the microstructureof 17-4 PH stainless steel. Mater Sci Eng, 104(C), pp. 181–189, 1988.

[7] A. Yadollahi, N. Shamsaei, S. M. Thompson, A. Elwany, and L. Bian, Mechanical andmicrostructural properties of selective laser melted 17-4 ph stainless steel. ASME InternationalMechanical Engineering Congress and Exposition, Proceedings (IMECE),2A-2015, 2015.

[8] J. H. Wu and C. K. Lin, Influence of high temperature exposure on the mechanical behaviorand microstructure of 17-4 PH stainless steel. J Mater Sci, 38(5), pp. 965–971,2003.

[9] H. Mirzadeh and A. Najafizadeh, Aging kinetics of 17-4 PH stainless steel. Mater ChemPhys, 116(1), pp. 119–124, 2009.

[10] W. E. Luecke and J. A. Slotwinski, Mechanical properties of austenitic stainless steelmade by additive manufacturing. J Res Natl Inst Stand Technol, 119, pp. 398–418,2014.

[11] B. Lozanovski et al., Computational modelling of strut defects in SLM manufacturedlattice structures. Mater Des, 171, 2019.

[12] X. Ren, J. Shen, P. Tran, T. D. Ngo, and Y. M. Xie, Design and characterisation of a tuneable3D buckling-induced auxetic metamaterial. Mater Des, 139, pp. 336–342, 2018.

[13] X. Ren, J. Shen, P. Tran, T. D. Ngo, and Y. M. Xie, Auxetic nail: design and experimentalstudy. Compos Struct, 184(2017), pp. 288–298, 2018.

[14] L. Yang, O. Harrysson, H. West, and D. Cormier, Correction to: modeling of uniaxialcompression in a 3D periodic re-entrant lattice structure. (Journal of Materials Science,48(4), pp. 1413–1422, 2013.,” J MaterSci, 55(21), p. 9144, 2020.

[15] M. Mahmoudi, A. Elwany, A. Yadollahi, S. M. Thompson, L. Bian, and N. Shamsaei,Mechanical properties and microstructural characterization of selective laser melted17-4 PH stainless steel. Rapid Prototyp J, 23(2), pp. 280–294. 2017.

[16] T. Maconachie et al., SLM lattice structures: properties, performance, applications andchallenges. Materials and Design, 183, 108137, 2019.

[17] P. Köhnen, C. Haase, J. Bültmann, S. Ziegler, J. H. Schleifenbaum, and W. Bleck, Mechanicalproperties and deformation behavior of additively manufactured lattice structuresof stainless steel. Mater Des, 145, pp. 205–217, 2018.

[18] F. Concli, A. Gilioli, and F. Nalli, Experimental–numerical assessment of ductile failureof additive manufacturing selective laser melting reticular structures made of Al A357.Proc Inst Mech Eng Part C J Mech Eng Sci, 32(7), pp. 3047–3056, 2019.

[19] F. Nalli, L. Cortese, and F. Concli, Ductile damage assessment of Ti6Al4V, 17-4PHand AlSi10Mg for additive manufacturing. Eng Fract Mech, 241, 2021.

[20] L. Bonaiti, F. Concli, C. Gorla, and F. Rosa, Bending fatigue behaviour of 17-4 PHgears produced via selective laser melting, Procedia StructIntegr, 24, pp. 764–774,2019.

[21] F. Concli and A. Gilioli, Numerical and experimental assessment of the static behaviorof 3D printed reticular Al structures produced by selective laser melting: progressivedamage and failure. Procedia Struct Integr, 12, pp. 204–212, 2018.

[22] W. Ramberg and W. R. Osgood, Description of stress–strain curves by three parameters.Washington, DC, 1943.

[23] J. R. Hollomon, Tensile deformation. Trans. AIME, 162, pp. 268–277, 1945.

[24] L. Maccioni, E. Rampazzo, F. Nalli, Y. Borgianni, and F. Concli, Low-cycle-fatigueproperties of a 17-4 PH stainless steel manufactured via selective laser melting. Materialand Manufacturing Technology XI, 877, pp. 55–60, 2021.

[25] L. Maccioni, L. Fraccaroli, and F. Concli, High-cycle-fatigue characterization of an additivemanufacturing 17-4 PH stainless steel. Key Engineering Materials, 2020.

[26] ASTM, “Standard practice for statistical analysis of linear or linearized stress-life (s–n)and strain-life (e–n) fatigue data. E ASTM 739-91, 2006.

[27] L. Maccioni, L. Fraccaroli, Y. Borgianni, and F. Concli, High-cycle-fatigue characterizationof an additive manufacturing 17-4 PH stainless steel. 11th International Conferenceon Materials and Manufacturing Technologies, 2020, p. MT013.

[28] Z. Alomar and F. Concli, A review of the selective laser melting lattice structuresand their numerical models. Adv Eng Mater, 22(12), 2020.


[30] A. Gilioli, et al., Numerical and experimental assessment of the mechanical propertiesof 3D printed 18-Ni300 steel trabecular structures produced by selective laser melting–a lean design approach. Virtual Phys Prototyp, pp. 267–276, 2019.


[32] L. Fraccaroli, et al., High and low cycle fatigue properties of 17-4 PH Manufacturedvia Selective Laser Melting in as-built, machined and hipped conditions. Progressin Additive Manufacturing, 7, pp. 99–109, 2021.

[33] Rampazzo E. et al., Design for additive manufacturing: is it an effective alternative?Part 1 – Material characterization and geometrical optimization. WIT Transaction inEngineering Sciences, 130, 2021.

[34] M. Molinaro et al., Design for additive manufacturing: is it an effective alternative? Part2 – cost evaluation. WIT Transaction in Engineering Sciences, 130, 2021.

[35] L. Bonaiti, et al., High and low-cycle-fatigue properties of 17–4 PH manufactured viaselective laser melting in as-built, machined and hipped conditions. Progress AdditiveManufacturing, 7, pp. 99–109, 2021.

[36] Gerosa R., et al., Bending fatigue behavior of 17-4 PH gears produced by additivemanufacturing. App Sci, 11(7), 3019, 2021.

[37] M. Molinaro et al., Design for additive manufacturing: is it an effective alternative? Part1 – material characterization and geometrical optimization. WIT Transaction in EngineeringSciences, 130, 2021.