Grey box modeling to predict tensile properties in laser powder bed fusion

Abstract

Additive Manufacturing is an upcoming technology to produce metal structures in industry as complex near net-shaped structures can be built. One commonly used method is Laser Powder Bed Fusion (PBF-LB), that uses lasers to melt metal powder layer by layer. Alongside advantages that come with this technology, the variety of adjustable process parameters is challenging for optimizing the process due to complex correlations of those. Additionally, the resulting mechanical properties can be challenging to optimize empirically based on the vast multi-dimensional parameter space. To solve this problem, several Machine Learning (ML) approaches have been used in the scope of different applications. With the aim of predicting tensile properties of PBF-LB parts, printed with AlSi10Mg0.5, three ML models (Linear Regression, XGBoost, and Multi-Layer Perceptrons (MLP)) were trained as black box models on a newly created dataset. To include as much process information as possible in the training data, an additional mathematically based dimensionless metrics model was used. The model gained insights into the thermodynamic conditions of certain parameter sets, which enabled a more even distribution of different processing conditions in the dataset. This equation-based white box model, combined with the subsequent ML-based black-box models formed the grey-box modeling approach. Due to the small size of the dataset of only 50 data vectors, strong overfitting was observed. This could be minimized for the case of the MLP by means of a customized architecture and strong regularization. It was shown, that the dataset enabled sufficient predictions by the MLP model of yield and tensile strength with $$\textit{R}^2_{test}$$ R test 2 values of up to 88.3 $$\%$$ % and 86.2 $$\%$$ % , respectively. Elongation at break turned out to be more challenging to predict with an $$\textit{R}^2_{test}$$ R test 2 of up to 75.6 $$\%$$ % . This highlighted the benefit of combining the phenomenological melt mode model for data creation and a ML model for sufficient prediction of mechanical properties in a resource efficient manner.