Machine learning based Melt Pool Spattering registration and defectcorrelation for LPBF additive manufacturing

Haolin Zhang, Chaitanya Krishna Prasad Vallabh, Xiayun Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Laser powder bed fusion (LPBF) additive manufacturing (AM) process has attracted increasing research and application interest in recent years, due to its potential ability to efficiently produce parts with complicated geometry. Despite the rapid development, LPBF manufactured products still lack the quality required for practical implementation in most industries. In LPBF printing processes, some major defects can be attributed to the melt pool (MP) induced spatters which would cause porosity, powder contamination, and recoater intervention. In this work, we develop a machine learning (ML) based method for automatically and accurately registering MP spattering metrics using in-situ off-axis camera monitoring data. Specifically, each monitored MP image is analyzed for extracting its associated spattering signatures including the MP's center location (coordinates), spatter count, and ejection angle relative to its MP center. Such MP spattering signatures are registered for each monitored MP across each print layer. To evaluate the potential influence of MP spattering on surface defects, a preliminary correlation of each print layer's registered MP spattering signatures to the layer's surface topography measured by a lab-designed in-situ Fringe Projection Profilometry (FPP) system is performed via ML. Overall, this work presents a systematic study on MP spattering monitoring, registration, and correlation to reveal effect on defect formation during LPBF, allowing for online qualification of LPBF-AM processes.

Original languageEnglish
Title of host publication2022 ASPE and euspen Summer Topical Meeting on Advancing Precision in Additive Manufacturing
Pages146-151
Number of pages6
ISBN (Electronic)9781713859192
StatePublished - 2022
Event2022 ASPE and euspen Summer Topical Meeting on Advancing Precision in Additive Manufacturing - Knoxville, United States
Duration: 11 Jul 202214 Jul 2022

Publication series

Name2022 ASPE and euspen Summer Topical Meeting on Advancing Precision in Additive Manufacturing

Conference

Conference2022 ASPE and euspen Summer Topical Meeting on Advancing Precision in Additive Manufacturing
Country/TerritoryUnited States
CityKnoxville
Period11/07/2214/07/22

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