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.
Laser-Powder Bed Fusion (L-PBF) is an additive manufacturing technique used to melt metal material into solid three-dimensional parts. While offering a high degree of design freedom, L-PBF still has technical restrictions, like the achievable surface roughness, resolution and the need for support structures in overhanging areas. [1]
Currently, L-PBF is used mainly to produce small batches of parts and prototypes. [2] In order to fully industrialize the technology, the research campus in Aachen is investigating possible future applications in turbomachinery while developing the corresponding processes with industry partners.
Sealing systems, like honeycomb seal strips in gas turbines often require time-consuming joining and assembly operations that can be avoided by building up the structure monolithically using L-PBF.
The following process development study proves the feasibility of manufacturing honeycombs with L-PBF using the Nickel-based super-alloy Inconel 718 (IN718) on an EOS M290 machine. Here, we have evaluated the economic aspects of different build orientations of the seal strips. Afterwards, we conducted a systematic parameter study with continuous and pulsed wave laser emission and investigated the resulting wall thicknesses. A reduction in wall thickness of about 30% can be observed when a modulated laser is used.
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