Abstract
Highly complex data streams from in-situ additive manufacturing (AM) monitoring systems are becoming increasingly prevalent, yet finding physically actionable patterns remains a key challenge. Recent AM literature utilising machine learning methods tend to make predictions about flaws or porosity without considering the dynamical nature of the process. This leads to increases in false detections as useful information about the signal is lost. This study takes a different approach and investigates learning a physical model of the laser powder bed fusion process dynamics. In addition, deep representation learning enables this to be achieved directly from high speed videos. This representation is combined with a predictive state space model which is learned in a semi-supervised manner, requiring only the optimal laser parameter to be characterised. The model, referred to as FlawNet, was exploited to measure offsets between predicted and observed states resulting in a highly robust metric, known as the dynamic signature. This feature also correlated strongly with a global material quality metric, namely porosity. The model achieved state-of-the-art results with a receiver operating characteristic (ROC) area under curve (AUC) of 0.999 when differentiating between optimal and unstable laser parameters. Furthermore, there was a demonstrated potential to detect changes in ultra-dense, 0.1% porosity, materials with an ROC AUC of 0.944, suggesting an ability to detect anomalous events prior to the onset of significant material degradation. The method has merit for the purposes of detecting out of process distributions, while maintaining data efficiency. Subsequently, the generality of the methodology would suggest the solution is applicable to different laser processing systems and can potentially be adapted to a number of different sensing modalities.
Highlights
As sensor data fidelity improves in additive manufacturing (AM) monitoring systems, so does the challenge of extracting useful and meaningful patterns efficiently
The cameras are mounted co-axial with the laser and the laser is measured in a Lagrangian perspective, where the camera is aligned with the laser as it moves
The method exploits the correlations between observed images and the laser input, as well as the time-series correlations between images to reduce the rate of false detections
Summary
As sensor data fidelity improves in additive manufacturing (AM) monitoring systems, so does the challenge of extracting useful and meaningful patterns efficiently. There is, a need for developing such causal dynamics models capable of exploiting underlying physical patterns, enabling detection of anomalous events, leading to on-line quality monitoring of the AM process. This need is evident in Laser Powder Bed Fusion (L-PBF), the most widely used metal 3D printing process. A thin layer of powder is spread across a build plate, which is fused with a rapidly scanning laser; the cycle is repeated until a 3D metal component is formed This enables an efficient manufacturing process, while minimising material and energy usage (Ford and Despeisse 2016). The mass production of end-use components would disrupt manufacturing as it is understood today (Attaran 2017)
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