Predicting gas pores from photodiode measurements in laser powder bed fusion builds

Sarini Jayasinghe*, Paolo Paoletti, Nick Jones, Peter L. Green

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
13 Downloads (Pure)


Recent studies in additive manufacturing (AM) monitoring techniques have focussed on the identification of defects using in situ monitoring sensor systems, with the aim of improving overall AM part quality. Much work has focussed on the use of of camera-based monitoring systems; however, limitations such as the slow response rates of the sensors (1-10kHz) and the post-processing requirements of the collected images make it difficult to apply these developmental monitoring methods on production systems in real-time. Furthermore, the replication of results from camera-based monitoring systems (often obtained using deep learning models) in a production environment is limited by the need for specialised hardware with high computational capacity (e.g GPUs). Focussing specifically on laser powder bed fusion ( PBF-L/M ), photodiodes, with fast data collection rates (50–100kHz) and providing data that is relatively easy to process are potentially better suited to real-time monitoring systems. The current study, therefore, focuses on using data collected from photodiodes to identify defects in PBF-L/M builds. A predictive model with real-time potential is proposed that, having been validated on data from computer tomography (CT) images, can be used to locate porosity within layers of PBF-L/M builds.
Original languageEnglish
Pages (from-to)885-888
Number of pages4
JournalProgress in Additive Manufacturing
Issue number4
Publication statusPublished - 28 Jul 2023


  • autoregressive model
  • laser powder bed fusion
  • real-time defect detection
  • time-series modelling


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