Demonstration of Closed-Loop Control for Laser Powder Bed Fusion (LPBF)
Currently, AM processes such as LPBF are performed open loop, using a fixed, preprogrammed definition of material deposition (path, speed, laser power, and so on). Actual layer and part formation details, even when measured, are not fed back to the print controller to account for actual, as-made lay...
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Published in | Structural Integrity of Additive Manufactured Materials and Parts pp. 1 - 19 |
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Main Author | |
Format | Book Chapter |
Language | English |
Published |
100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959
ASTM International
01.09.2020
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Online Access | Get full text |
ISBN | 9780803177086 0803177089 |
DOI | 10.1520/STP163120190130 |
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Abstract | Currently, AM processes such as LPBF are performed open loop, using a fixed, preprogrammed definition of material deposition (path, speed, laser power, and so on). Actual layer and part formation details, even when measured, are not fed back to the print controller to account for actual, as-made layer conditions. Unanticipated layer and part deviations occur frequently that, in the worst case, can result in print failure, part rejection, higher scrap rate, lower yield, and more expensive parts. Process anomalies are sometimes detected manually by the operator. In-process inspection methods such as melt pool monitoring typically do not provide accept/reject guidance. When anomalies are noted, no instructions are provided to the operator or the machine to repair or compensate for the flaw and to salvage the build, in cases where this is possible. We demonstrate development of a closed-loop control capability using a nonthermal in-process inspection method on every layer. Layer Topographic Mapping (LTM) is an in-process inspection method using an optical profilometer to generate a dense, precise map of layer surface height. Algorithms process this data to detect melt flaws with excellent performance. Demonstrated detection of lack of fusion flaws in more than 1,800 Inconel 625 layers is 98.2% probability of detection (POD) and 1.0% probability of false detection (POFD). Optimum repair/rework processes were developed for lack of fusion flaw regions one to three layers thick. LTM software was modified to not only detect flaws but also to define the optimum repair process to employ upon detection based on the number of flaw layers present. Intentionally created (or seeded) lack of fusion flaws were restored to less than 0.1% porosity for one- to three-layer flaws. Porosity in the flaw regions was reduced by up to 98% as verified by CT scans. |
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AbstractList | Currently, AM processes such as LPBF are performed open loop, using a fixed, preprogrammed definition of material deposition (path, speed, laser power, and so on). Actual layer and part formation details, even when measured, are not fed back to the print controller to account for actual, as-made layer conditions. Unanticipated layer and part deviations occur frequently that, in the worst case, can result in print failure, part rejection, higher scrap rate, lower yield, and more expensive parts. Process anomalies are sometimes detected manually by the operator. In-process inspection methods such as melt pool monitoring typically do not provide accept/reject guidance. When anomalies are noted, no instructions are provided to the operator or the machine to repair or compensate for the flaw and to salvage the build, in cases where this is possible. We demonstrate development of a closed-loop control capability using a nonthermal in-process inspection method on every layer. Layer Topographic Mapping (LTM) is an in-process inspection method using an optical profilometer to generate a dense, precise map of layer surface height. Algorithms process this data to detect melt flaws with excellent performance. Demonstrated detection of lack of fusion flaws in more than 1,800 Inconel 625 layers is 98.2% probability of detection (POD) and 1.0% probability of false detection (POFD). Optimum repair/rework processes were developed for lack of fusion flaw regions one to three layers thick. LTM software was modified to not only detect flaws but also to define the optimum repair process to employ upon detection based on the number of flaw layers present. Intentionally created (or seeded) lack of fusion flaws were restored to less than 0.1% porosity for one- to three-layer flaws. Porosity in the flaw regions was reduced by up to 98% as verified by CT scans. |
Author | Maass, David |
Author_xml | – sequence: 1 givenname: David surname: Maass fullname: Maass, David organization: Flightware, Inc |
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ContentType | Book Chapter |
Copyright | All rights reserved. This material may not be reproduced or copied, in whole or part, in any printed, mechanical, electronic, film, or other distribution and storage media, without the written consent of the publisher. 2020 ASTM International 2020 |
Copyright_xml | – notice: All rights reserved. This material may not be reproduced or copied, in whole or part, in any printed, mechanical, electronic, film, or other distribution and storage media, without the written consent of the publisher. 2020 ASTM International – notice: 2020 |
DOI | 10.1520/STP163120190130 |
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Notes | 2019-10-07 - 2019-10-10Fourth ASTM Symposium on Structural Integrity of Additive Manufactured Materials and PartsFort Washington, MD |
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References | Federal Aviation Administration, A Quantitative Assessment of Conventional Nondestructive Inspection Techniques for Detecting Flaws in Composite Laminate Aircraft Structures, DOT/FAA/TC-15/6 (Washington, DC: FAA, 2016). KellyS. M. , BoulwareP. C. , CronleyL. , FirestoneG. , JamshidiniaM. , MarchalJ. , StempkyT. , and ReichertC. , “In-Process Sensing of Laser Powder Bed Fusion Additive Manufacturing,” (paper presentation, Workshop on Predictive Theoretical and Computational Approaches for Additive Manufacturing, Washington, DC, October 7–9, 2015). NassarA. R. , KeistJ. S. , ReutzelE. W. , and SpurgeonT. J. , “Intra-Layer Closed-Loop Control of Build Plan during Directed Energy Additive Manufacturing of Ti–6Al–4V,” Additive Manufacturing 6 (2015): 39–52. “Weibull Distribution,” Wikpedia, https://perma.cc/N4WU-LEFJ U.S. Department of Defense, Nondestructive Evaluation System Reliability Assessment, MIL-HDBK-1823A (Washington, DC: U.S. Department of Defense, 2009). RenkenV. , LübbertL. , BlomH. , von FreybergA. , and FischerA. , “Model Assisted Closed-Loop Control Strategy for Selective Laser Melting,” Procedia CIRP 74 (2018): 659–663. TimmF. and BarthE. , “Non-Parametric Texture Defect Detection Using Weibull Features,” in Proceedings Volume 7877, Image Processing: Machine Vision Applications IV (Bellingham, WA: SPIE, 2011). CheridoL. , “Metal 3D Printer Market in 2019,” https://perma.cc/UA5N-RZG6 MirelesJ. , RidwanS. , MortonP. A. , HinojosA. , and WickerR. B. , “Analysis and Correction of Defects within Parts Fabricated Using Powder Bed Fusion Technology,” Surface Topography: Metrology and Properties 3, no. 3 (2015), https://doi.org/10.1088/2051-672X/3/3/034002 |
References_xml | – reference: “Weibull Distribution,” Wikpedia, https://perma.cc/N4WU-LEFJ – reference: Federal Aviation Administration, A Quantitative Assessment of Conventional Nondestructive Inspection Techniques for Detecting Flaws in Composite Laminate Aircraft Structures, DOT/FAA/TC-15/6 (Washington, DC: FAA, 2016). – reference: CheridoL. , “Metal 3D Printer Market in 2019,” https://perma.cc/UA5N-RZG6 – reference: KellyS. M. , BoulwareP. C. , CronleyL. , FirestoneG. , JamshidiniaM. , MarchalJ. , StempkyT. , and ReichertC. , “In-Process Sensing of Laser Powder Bed Fusion Additive Manufacturing,” (paper presentation, Workshop on Predictive Theoretical and Computational Approaches for Additive Manufacturing, Washington, DC, October 7–9, 2015). – reference: TimmF. and BarthE. , “Non-Parametric Texture Defect Detection Using Weibull Features,” in Proceedings Volume 7877, Image Processing: Machine Vision Applications IV (Bellingham, WA: SPIE, 2011). – reference: RenkenV. , LübbertL. , BlomH. , von FreybergA. , and FischerA. , “Model Assisted Closed-Loop Control Strategy for Selective Laser Melting,” Procedia CIRP 74 (2018): 659–663. – reference: NassarA. R. , KeistJ. S. , ReutzelE. W. , and SpurgeonT. J. , “Intra-Layer Closed-Loop Control of Build Plan during Directed Energy Additive Manufacturing of Ti–6Al–4V,” Additive Manufacturing 6 (2015): 39–52. – reference: MirelesJ. , RidwanS. , MortonP. A. , HinojosA. , and WickerR. B. , “Analysis and Correction of Defects within Parts Fabricated Using Powder Bed Fusion Technology,” Surface Topography: Metrology and Properties 3, no. 3 (2015), https://doi.org/10.1088/2051-672X/3/3/034002 – reference: U.S. Department of Defense, Nondestructive Evaluation System Reliability Assessment, MIL-HDBK-1823A (Washington, DC: U.S. Department of Defense, 2009). |
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Snippet | Currently, AM processes such as LPBF are performed open loop, using a fixed, preprogrammed definition of material deposition (path, speed, laser power, and so... |
SourceID | knovel astm |
SourceType | Publisher Enrichment Source |
StartPage | 1 |
SubjectTerms | Additive Manufacturing Closed-Loop Control Defect Repair Defects Flaws In Situ Inspection In-Process Inspection Lack Of Fusion Laser Powder Based Fusion (lpbf) Manufacturing Engineering Materials & Manufacturing Processes Surface Profilometry |
TableOfContents | 1.1 Introduction
1.2 Surface Profilometry Data Acquisition
1.3 Test Coupon Fabrication
1.4 Validation of Flaws via CT Scan
1.5 Automated Flaw Detection and Repair Demonstration (Closed-Loop Control)
1.6 Results and Discussion
1.7 Conclusions
Acknowledgments
References |
Title | Demonstration of Closed-Loop Control for Laser Powder Bed Fusion (LPBF) |
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