AI-DRIVEN PREDICTIVE MODELLING FOR LASER METAL DEPOSITION

Perani, Martina and Baraldo, Stefano and Decker, Michael and Vandone, Ambra and Valente, Anna and Paoli, Beatrice (2021) AI-DRIVEN PREDICTIVE MODELLING FOR LASER METAL DEPOSITION. Working Paper (Submitted)

[img] Text
LMD_CNN___RCIM.pdf

Download (1MB)

Abstract

Laser Metal Deposition (LMD) is an additive manufacturing technology that attracts great interest from the industry, thanks to its potential to realize parts with complex geometries in one piece, and to repair damaged ones, while maintaining good mechanical properties. Nevertheless, the complexity of this process has limited its widespread adoption, since different part geometries, strategies and boundary conditions can yield very different results in terms of external shapes and inner flaws. Moreover, monitoring part quality during the process execution is very challenging, as direct measurements of both structural and geometrical properties are mostly impracticable. This work proposes an on-line monitoring and prediction approach for LMD that exploits coaxial melt pool images, together with process input data, to estimate the size of a track deposited by LMD. In particular, a novel deep learning architecture combines the output of a convolutional neural network (that takes melt pool images as inputs) with scalar variables (process and trajectory data). Various network architectures are evaluated, suggesting to use at least three convolutional layers. Furthermore, results imply a certain degree of invariance to the number and size of dense layers. This nonlinear regression model can be used as the basis for a model predictive process controller, which is a key enabler for improving the quality of LMD parts in general cases. The effectiveness of the proposed method is demonstrated basing on experiments performed on single tracks deposited by LMD using powders of Inconel 718, a relevant material for the aerospace and automotive sectors.

Actions (login required)

View Item View Item