DynamicPrint: A physics-guided feedforward model predictive process control approach for defect mitigation in laser powder bed fusion additive manufacturing

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dc.contributor.authorRiensche, Alexen
dc.contributor.authorBevans, Benjaminen
dc.contributor.authorCarrington Jr, Antonioen
dc.contributor.authorDeshmukh, Kaustubhen
dc.contributor.authorShephard, Kamdenen
dc.contributor.authorSions, Johnen
dc.contributor.authorSynder, Kyleen
dc.contributor.authorPlotnikov, Yurien
dc.contributor.authorCole, Kevinen
dc.contributor.authorRao, Prahaladaen
dc.date.accessioned2025-02-18T13:01:11Zen
dc.date.available2025-02-18T13:01:11Zen
dc.date.issued2025-01-05en
dc.description.abstractIn this work, we developed and applied a physics-guided autonomous feedforward model predictive process control approach called DynamicPrint to mitigate part defects in laser powder bed fusion (LPBF) additive manufacturing. Currently, the processing parameters for LPBF of a specific material are optimized through empirical testing of simple-shaped coupons. These optimized parameters are typically maintained constant when printing complex parts. However, using constant parameters often causes uneven temperature distribution in complex parts, leading to such defects as inhomogeneous microstructure, poor surface finish, thermal-induced distortion, and build failures. By contrast, DynamicPrint autonomously adjusts the processing parameters layer-by-layer before an LPBF part is printed to prevent non-uniform temperature distribution and mitigate thermal-induced defects. The a priori process parameter adjustments in DynamicPrint are guided by rapid physics-based thermal simulations. Through experiments with complex stainless steel 316 L LPBF parts, we demonstrate the following beneficial outcomes of DynamicPrint: (i) homogenous grain sizes and consistent properties (microhardness); (ii) improved geometric accuracy and surface integrity of hard-to-access internal features; and (iii) avoidance of recoater crashes and elimination of supports in parts with prominent overhang features. DynamicPrint can greatly accelerate the time-to-market for LPBF parts by offering a rapid, physics-based method for process qualification, unlike the current cumbersome and expensive empirical build-and-test approach.en
dc.description.versionAccepted versionen
dc.format.extent23 page(s)en
dc.identifierARTN 104592 (Article number)en
dc.identifier.doihttps://doi.org/10.1016/j.addma.2024.104592en
dc.identifier.eissn2214-7810en
dc.identifier.issn2214-8604en
dc.identifier.orcidRao, Prahalada [0000-0002-9642-622X]en
dc.identifier.urihttps://hdl.handle.net/10919/124614en
dc.identifier.volume97en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectLaser powder bed fusion (LPBF)en
dc.subjectThermal historyen
dc.subjectPhysics-based feedforward process controlen
dc.subjectModel predictive controlen
dc.titleDynamicPrint: A physics-guided feedforward model predictive process control approach for defect mitigation in laser powder bed fusion additive manufacturingen
dc.title.serialAdditive Manufacturingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Engineeringen
pubs.organisational-groupVirginia Tech/Engineering/Industrial and Systems Engineeringen
pubs.organisational-groupVirginia Tech/All T&R Facultyen
pubs.organisational-groupVirginia Tech/Engineering/COE T&R Facultyen

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