Abstract
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.
Highlights
The same graph shows the training performance based on high dynamic range (HDR) image patches and low dynamic range (LDR) patches for input
In the presented work, a new approach based on HDR imaging combined with convolutional neural networks (CNN) and model-based reinforcement learning (RL) for inter-layer quality optimization of laser powder bed fusion (LPBF) processes is proposed
The following experimental results are encouraging to continue and improve the demonstrated concept: 1) Surface roughness classification based on optical imaging and deep neural networks outperforms a classical machine learning (ML) approach using statistical texture features under the same image resolution and dynamic range conditions by more than 20 % in F1-Score
Summary
A. PROCESS PARAMETER AND SURFACE ROUGHNESS As a sub-branch based of AM, the laser powder bed fusion (LPBF) technology is frequently used in machine tool and automotive industries [1], in aerospace engineering [2] as well as for medical devices [3]. LPBF is considered one of the key technologies that enables the fabrication of increasingly complex parts and systems with high demands on mechanical properties (e.g., yield strength, ductility, or heat resistance) [4]. A significant quality parameter in LPBF is the increased roughness of the as-built surfaces, which potentially leads to reduced fatigue life of the final part due to the concentration of residual stresses on the surfaces [8]. High surface roughness generally leads to poor surface quality and requires long and expensive post-finishing operations. The final part surface is often specified to be in range of the roughness defined by the current application which can require a surface roughness of 0.8 μm or better to prevent mechanical failure of the part due to cracks initiating on its surface [9]
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