Journal
IEEE ACCESS
Volume 9, Issue -, Pages 55214-55231Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3067302
Keywords
Surface roughness; Rough surfaces; Surface treatment; Surface emitting lasers; Surface topography; Surface texture; Optimization; Laser powder bed fusion; convolutional neural networks; model-based reinforcement learning; high dynamic range imaging; surface roughness optimization
Categories
Funding
- European Union [825030]
- H2020 Societal Challenges Programme [825030] Funding Source: H2020 Societal Challenges Programme
Ask authors/readers for more resources
A novel analysis approach combining HDR optical imaging and CNN is proposed for predicting the surface roughness of LPBF parts, with the introduction of reinforcement learning for process parameter optimization. Experimental data indicate the practical applicability of the method in industrial environments.
In laser-based additive manufacturing (AM) of metal parts from powder bed, information about actual part quality obtained during build is essential for cost-efficient production and high product quality. Reliable and effective monitoring strategies for laser powder bed fusion (LPBF) therefore remain in high demand and are the subject of current research. To address this demand, a novel analysis approach using high dynamic range (HDR) optical imaging in combination with convolutional neural networks (CNN) is proposed for spatially resolved and layer-wise prediction of the surface roughness of LPBF parts. In a further step, the predicted surface roughness maps are used as a feedback signal for a reinforcement learning technique that employs a dynamics model to subsequently identify optimal process parameters under varying and uncertain conditions. The proposed approach ultimately combines the estimation of the local surface roughness based on image texture and model-based reinforcement learning to an in-situ optimization framework for LPBF processes. In addition, the relationship between the layer surface roughness of the part and the overall part density is discussed on the basis of experimental data, which also indicate the applicability of the proposed method in industrial environments. This preliminary study is a first step towards highly adaptive and intelligent machines in the field of automated laser powder bed fusion with the primary goals of reducing production costs and improving the environmental fingerprint as well as print quality.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available