Knowledge-transfer-enabled porosity prediction for new part geometry in laser metal deposition

Data-driven porosity prediction in Laser Metal Deposition (LMD) is mainly done with supervised machine learning methods. These methods require labeled thermal signatures for model training, with the “labels” being post-process evaluations of porosity. In practice, acquiring porosity records for newly printed parts is expensive and time-consuming; matching thermal signatures from the printing process with the porosity records is subject to data registration errors. To enable convenient porosity prediction for new part geometry, this study proposes a “knowledge transfer” method to transfer prior statistical knowledge about printed parts to new printing processes. The prior knowledge is leveraged to evaluate the statistical property of new thermal signatures and assign them labels. Supervised machine learning methods can be readily trained with labeled data. The effort for post-process porosity inspection is therefore saved, and the efficiency of data-driven porosity prediction is significantly improved. The proposed method is validated with datasets from an LMD machine, specifically an OPTOMEC LENS 750 system. The statistical inference knowledge about a Ti-6Al-4V thin wall is transferred to two different Ti-6Al-4V cylinders, respectively, to label their thermal signatures and train Convolutional Neural Networks (CNNs) for in-situ porosity prediction. The case study results demonstrate the effectiveness of the proposed method.