Bayesian Inference of Visco-Elastic Visco-Plastic Material Model Parameters for SLS-printed polyamide lattices

The response of polymeric materials can be represented using complex finite-strain visco-elastic visco-plastic material models. Such a model requires the identification of tens of parameters in order to remain accurate for a wide range of strain rates and stress states, the response in compression being different than in tension. A complex experimental campaign involving dynamic mechanical analysis (DMA), and compressive and tensile cyclic loading at different strain rates is thus required. Besides, when considering lattice structures obtained by additive manufacturing, the struts response is not similar to the macro-bulk material response. Because a complex experimental campaign cannot be conducted at the level of the struts, the parameters identification also needs to be conducted at the level of the lattice response. However, when performing the parameters identification using these different loading cases, a unique set of parameters cannot usually reproduce all the experimental tests because of the model limitations and errors, in particular when considering nonlinear responses. Besides, the data are inevitably entailed by experimental errors. These difficulties can be circumvented by considering a Bayesian Inference (BI) process. In this presentation we consider experimental tests conducted at different scales on polyamide lattices in order to infer the model parameters of a complex finite-strain visco-elastic visco-plastic material model.Multiscale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials (MOAMMM

Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator

peer reviewedIn this work, the parameters of a finite-strain visco-elastic-visco-plastic formulation with pressure dependency in both the visco-elastic and visco-plastic parts are identified using as observations experimental data obtained from tension and compression tests at different strain rates ranging from 10^-4 s^-1 to 10^3 s^-1 . Because of the high number of parameters of the model, a sequential Bayesian Inference (SBI) framework with data augmentation, which presents several advantages, is developed. First the sequential nature reduces the difficulty of selecting the appropriate prior distributions by considering only parts of the observations at a time. Second, the sequential nature prevents dealing with low likelihood values by considering only a part of the experimental observations at a time, but also subsets of the material parameters to be identified, improving the convergence of the Markov Chain Monte Carlo (MCMC) random walk. Third, the data augmentation allows considering different number of experimental tests in tension and in compression while preserving the identified model accuracy for both loading modes. This SBI is carried out to infer the properties of Polyamide 12 (PA12) processed by Selective Laser Sintering (SLS) for two different printing directions and it is shown that the models fed by their respective set of inferred parameters can reproduce the different experimental tests. Finally, in order for upcoming structural simulations to benefit from the information related to the uncertainties due to the measurement errors, the identification process and the model limitations, we introduce a Generative Adversarial Network (GAN), which is trained using the data obtained from MCMC random walk. This generators can then serve to produce a synthetic data-set of arbitrary size of the material parameters to be used in finite-element simulations.Multiscale Optimisation for Additive Manufacturing of fatigue resistant shock-absorbing MetaMaterials (MOAMMM