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

Abstract

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 a 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 ( $$Z_s$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>Z</mml:mi> <mml:mi>s</mml:mi> </mml:msub> </mml:math> ) 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 $$Z_s$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>Z</mml:mi> <mml:mi>s</mml:mi> </mml:msub> </mml:math> , and used for training. The best hybrid algorithm trained performed with a Mean Average Error (MAE) of 0.266 and a Mean Absolute Percentage Error (MAPE) of $$6.7\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>6.7</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> . 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.

Topics & Concepts