A hybrid deep learning model of process-build interactions in additive manufacturing

Laser powder bed fusion (LPBF) is a technique of additive manufacturing (AM) that is often used to construct a metal object layer-by-layer. The quality of AM builds depends to a great extent on the minimization of different defects such as porosity and cracks that could occur by process deviation during machine operation. Therefore, there is a need to develop new analytical methods and tools to equip the LPBF process with the inspection frameworks that assess the process condition and monitor the porosity defect in real-time. Advanced sensing is recently integrated with the AM machines to cope with process complexity and improve information visibility. This opportunity lays the foundation for online monitoring and assessment of the in-process build layer. This study presents the hybrid deep neural network structure with two types of input data to monitor the process parameters that result in porosity defect in cylinders’ layers. Results demonstrate that statistical features extracted by wavelet transform and texture analysis along with original powder bed images, assist the model in reaching a robust performance. In order to illustrate the fidelity of the proposed model, the capability of the main pipeline is examined and compared with different machine learning models. Eventually, the proposed framework identified the process conditions with an F-score of 97.14%. This salient flaw detection ability is conducive to repair the defect in real-time and assure the quality of the final part before the completion of the process.     

Track geometry prediction for Laser Metal Deposition based on on-line artificial vision and deep neural networks

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. 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.