A hybrid machine learning model for in-process estimation of printing distance in laser Directed Energy Deposition

Abstract

There are several parameters that highly influence material quality and printed shape in laser Directed Energy Deposition (L-DED) operations. These parameters are usually defined for an optimal combination of energy input (laser power, scanning speed) and material feed rate, providing ideal bead geometry and layer height to the printing setup. However, during printing, layer height can vary. Such variation affects the upcoming layers by changing the printing distance, inducing printing to occur in defocus zone then cumulatively increasing shape deviation. In order to address such issue, this paper proposes a novel intelligent hybrid method for in-process estimating the printing distance ( \(Z_s\) ) from melt pool images acquired during L-DED. The proposed hybrid method uses transfer learning to combine pre-trained Convolutional Neural Network (CNN) and Support Vector Regression (SVR) for an accurate yet computationally fast methodology. A dataset with \(2,700\) melt pool images was generated from the deposition of lines, at \(60\) different values of $Z_s$, and used for training. The best hybrid algorithm trained performed with a Mean Average Error (MAE) of \(0.266\) , which indicates an average target error of \(6.7%\) . The deployment of this algorithm in an application dataset allowed the printing distance to be estimated and the final part geometry to be inferred from the data. Thus, the present method can aid on-line feedback control on the Z-axis increment, to regulate layer height, improving 3D shape geometry in L-DED.