Automatic quality assessments of laser powder bed fusion builds from photodiode sensor measurements

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Abstract
While Laser powder bed fusion (L-PBF) machines have greatly improved in recent years, the L-PBF process is still susceptible to several types of defect formation. Among the monitoring methods that have been explored to detect these defects, camera-based systems are the most prevalent. However, using only photodiode measurements to monitor the build process has potential benefits, as photodiode sensors are cost-efficient and typically have a higher sample rate compared to cameras. This study evaluates whether a combination of photodiode sensor measurements, taken during L-PBF builds, can be used to predict measures of the resulting build quality via a purely data-based approach. Using several unsupervised clustering approaches build density is classified with up to 93.54% accuracy using features extracted from three different photodiodes, as well as observations relating to the energy transferred to the material. Subsequently, a supervised learning method (Gaussian Process regression) is used to directly predict build density with a RMS error of 3.65%. The study, therefore, shows the potential for machine-learning algorithms to predict indicators of L-PBF build quality from photodiode build measurements only. This study also shows that, relative to the L-PBF process parameters, photodiode measurements can contribute to additional information regarding L-PBF part quality. Moreover, the work herein describes approaches that are predominantly probabilistic, thus facilitating uncertainty quantification in machine-learnt predictions of L-PBF build quality.

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