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

Jayasinghe, Sarini and Paoletti, Paolo and Sutcliffe, Chris and Dardis, John and Jones, Nick and Green, Peter L. (2022) Automatic quality assessments of laser powder bed fusion builds from photodiode sensor measurements. Progress in Additive Manufacturing, 7 (2). pp. 143-160. ISSN 2363-9520 (https://doi.org/10.1007/s40964-021-00219-w)

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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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