Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication
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https://doi.org/10.1007/s11837-020-04438-4 Ó 2020 The Minerals, Metals & Materials Society
TECHNICAL ARTICLE
Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication BERKCAN KAPUSUZOGLU
1
and SANKARAN MAHADEVAN
1,2
1.—Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN 37235, USA. 2.—e-mail: [email protected]
This article investigates several physics-informed and hybrid machine learning strategies that incorporate physics knowledge in experimental data-driven deep-learning models for predicting the bond quality and porosity of fused filament fabrication (FFF) parts. Three types of strategies are explored to incorporate physics constraints and multi-physics FFF simulation results into a deep neural network (DNN), thus ensuring consistency with physical laws: (1) incorporate physics constraints within the loss function of the DNN, (2) use physics model outputs as additional inputs to the DNN model, and (3) pretrain a DNN model with physics model input-output and then update it with experimental data. These strategies help to enforce a physically consistent relationship between bond quality and tensile strength, thus making porosity predictions physically meaningful. Eight different combinations of the above strategies are investigated. The results show how the combination of multiple strategies produces accurate machine learning models even with limited experimental data.
INTRODUCTION Achieving the desired material properties and product quality in additive manufacturing (AM) processes has been studied using trial-and-error experiments as well as process models [either physics-based or machine learning (ML) models]. In a trial-and-error approach, the AM process is repeated multiple times with different process parameter combinations to achieve the desired microstructure and properties of the manufactured parts; this is expensive and time-consuming. Moreover, this trial-and-error approach needs to be implemented every time a new design needs to be manufactured. Therefore, in recent years, research efforts have focused on model-based methods for optimizing the AM process parameters. Several physics-based models have been developed depending on the AM process category and the quantity of interest (QoI).4 Costa et al.3 proposed an analytical solution for transient heat transfer during the printing process in fused filament fabrication (FFF). Different models have been proposed in
(Received June 3, 2020; accepted October 5, 2020)
the literature to study polymer sintering.6,7,9,17 Many of these models are parametric representations of complex physical processes based on various approximations. The parameters of such physicsbased models as well as the model errors need to be calibrated for each AM process using available observation data to reduce the uncertainty in the model predictions,11,15,16 Due to the complex physics of the AM process, a different model is needed for each sub-stage or phenomenon in th
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