Prediction of the powder catchment efficiency based on off-axial powder monitoring and coaxial melt pool monitoring in laser directed energy deposition

Abstract In the laser directed energy deposition process, the powder-melt pool interaction process critically influences the part quality. This study establishes the relationship between powder distribution, melt pool behavior and powder catchment efficiency (PCE) through image processing. A novel PCE prediction model is proposed based on off-axial powder monitoring and coaxial melt pool monitoring. The method consists of two key algorithms: one for extracting powder incidence ratio (PIR) from high-speed images and another for extracting area coefficient( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:msub> <mml:mi>k</mml:mi> <mml:mrow> <mml:mrow> <mml:mtext>area</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:mrow> </mml:math> ) from coaxial thermal images. A powder distribution probability map was proposed to describe the powder distribution, and based on this, the relationship between powder distribution characteristics (e.g. PIR, focal point, and focal length) and process parameters was analyzed. The impact of real printing conditions on powder distribution was also examined. Through the spatial registration of off-axial melt pool images and coaxial melt pool thermal images, the impact of melt pool temperature distribution on the powder catchment process was clarified. The impact of process parameters on the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:msub> <mml:mi>k</mml:mi> <mml:mrow> <mml:mrow> <mml:mtext>area</mml:mtext> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:mrow> </mml:math> and PCE was analyzed systematically. Finally, the effectiveness of the proposed prediction model is validated through PCE extracted from laser line-scanned point cloud data and a root mean squared error of 3.06% is achieved.