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DOE OSTI · 3030553

Bayesian optimization for inverse calibration of expensive computer models: A case study for Johnson-Cook model in machining

Karandikar, Jaydeep·Chaudhuri, Anirban·No, Timothy·Smith, Scott·Schmitz, Tony
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Abstract

Inverse model calibration for identifying the constitutive model parameters can be computationally demanding for expensive-to-evaluate simulation models. Here, this paper presents a modified Bayesian optimization (BO) method, denoted as BO-bound, that incorporates theoretical bounds on the quantity of interest. A case study for the inverse calibration of the Johnson Cook (J-C) flow stress model parameters is presented using machining (cutting) force data. The results show fast calibration of the five J-C parameters within 25 simulations. In general, the BO-bound method is applicable for inverse calibration of any expensive simulation models as well as optimization problems with known bounds.

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Bayesian optimizationBounded objective functionFinite element model calibrationInverse model calibrationMachining

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Karandikar, Jaydeep, Chaudhuri, Anirban, No, Timothy, Smith, Scott, Schmitz, Tony. 2022-03-04. Bayesian optimization for inverse calibration of expensive computer models: A case study for Johnson-Cook model in machining. https://doi.org/10.1016/j.mfglet.2022.02.001

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