LPBF Right the First Time—the Right Mix Between Modeling and Experiments
Laser powder bed fusion (LPBF) is an additive manufacturing process with many adjustable input parameters that directly affect manufacturability and quality of the final product. The selection of the optimal input parameters makes the process qualification and part certification a costly and time-co...
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Veröffentlicht in: | Integrating materials and manufacturing innovation 2019-06, Vol.8 (2), p.194-216 |
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creator | Megahed, Mustafa Mindt, Hans-Wilfried Willems, Jöerg Dionne, Paul Jacquemetton, Lars Craig, James Ranade, Piyush Peralta, Alonso |
description | Laser powder bed fusion (LPBF) is an additive manufacturing process with many adjustable input parameters that directly affect manufacturability and quality of the final product. The selection of the optimal input parameters makes the process qualification and part certification a costly and time-consuming task if performed using the traditional sequential and empirical approach.
Within the scope of the DARPA open manufacturing program, a rapid qualification framework is developed that relies on parallel multi-physics modeling and experimental efforts for verification and validation of the process input parameters during process development and material characterization. Product manufacturability is tested a priori via modeling and in-process monitoring is deployed to ensure input parameters are rapidly screened, and an optimal process window is selected. Process consistency and repeatability is further ensured through process characterization, process qualification, and via quantitative analysis of digital In-Process Quality Metrics™ (IPQM®s).
This paper discusses the rapid qualification methodology, model validation, and the application of the framework towards manufacturing of a challenging part defined by AFRL. The combination of numerical predictions, experimental refinement, and in-process monitoring delivered the first print right at first trial. Distortions are within predictions, geometric accuracy is within expectations, and quantitative metallurgical analysis shows dense as-built material with properties expected to fulfill performance requirements. In-process monitoring results provide a quantitative, digital Quality Signature™ or Digital Quality Record™ of process consistency and product quality. |
doi_str_mv | 10.1007/s40192-019-00133-8 |
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Within the scope of the DARPA open manufacturing program, a rapid qualification framework is developed that relies on parallel multi-physics modeling and experimental efforts for verification and validation of the process input parameters during process development and material characterization. Product manufacturability is tested a priori via modeling and in-process monitoring is deployed to ensure input parameters are rapidly screened, and an optimal process window is selected. Process consistency and repeatability is further ensured through process characterization, process qualification, and via quantitative analysis of digital In-Process Quality Metrics™ (IPQM®s).
This paper discusses the rapid qualification methodology, model validation, and the application of the framework towards manufacturing of a challenging part defined by AFRL. The combination of numerical predictions, experimental refinement, and in-process monitoring delivered the first print right at first trial. Distortions are within predictions, geometric accuracy is within expectations, and quantitative metallurgical analysis shows dense as-built material with properties expected to fulfill performance requirements. In-process monitoring results provide a quantitative, digital Quality Signature™ or Digital Quality Record™ of process consistency and product quality.</description><identifier>ISSN: 2193-9764</identifier><identifier>EISSN: 2193-9772</identifier><identifier>DOI: 10.1007/s40192-019-00133-8</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Additive Manufacturing Benchmarks 2018 ; Characterization and Evaluation of Materials ; Chemistry and Materials Science ; Consistency ; Construction materials ; Empirical analysis ; Geometric accuracy ; Manufacturability ; Manufacturing ; Materials Science ; Mathematical models ; Metallic Materials ; Metallurgical analysis ; Monitoring ; Nanotechnology ; Powder beds ; Process parameters ; Quality ; Quantitative analysis ; Structural Materials ; Surfaces and Interfaces ; Thematic Section: Additive Manufacturing Benchmarks 2018 ; Thin Films</subject><ispartof>Integrating materials and manufacturing innovation, 2019-06, Vol.8 (2), p.194-216</ispartof><rights>The Minerals, Metals & Materials Society 2019</rights><rights>Integrating Materials and Manufacturing Innovation is a copyright of Springer, (2019). All Rights Reserved.</rights><rights>2019© The Minerals, Metals & Materials Society 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2628-2323753b639c8045d9c415be9d8bf2e069f7d7e80027e948b800bdfd6cc8a2eb3</citedby><cites>FETCH-LOGICAL-c2628-2323753b639c8045d9c415be9d8bf2e069f7d7e80027e948b800bdfd6cc8a2eb3</cites><orcidid>0000-0003-3880-6483</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s40192-019-00133-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s40192-019-00133-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Megahed, Mustafa</creatorcontrib><creatorcontrib>Mindt, Hans-Wilfried</creatorcontrib><creatorcontrib>Willems, Jöerg</creatorcontrib><creatorcontrib>Dionne, Paul</creatorcontrib><creatorcontrib>Jacquemetton, Lars</creatorcontrib><creatorcontrib>Craig, James</creatorcontrib><creatorcontrib>Ranade, Piyush</creatorcontrib><creatorcontrib>Peralta, Alonso</creatorcontrib><title>LPBF Right the First Time—the Right Mix Between Modeling and Experiments</title><title>Integrating materials and manufacturing innovation</title><addtitle>Integr Mater Manuf Innov</addtitle><description>Laser powder bed fusion (LPBF) is an additive manufacturing process with many adjustable input parameters that directly affect manufacturability and quality of the final product. The selection of the optimal input parameters makes the process qualification and part certification a costly and time-consuming task if performed using the traditional sequential and empirical approach.
Within the scope of the DARPA open manufacturing program, a rapid qualification framework is developed that relies on parallel multi-physics modeling and experimental efforts for verification and validation of the process input parameters during process development and material characterization. Product manufacturability is tested a priori via modeling and in-process monitoring is deployed to ensure input parameters are rapidly screened, and an optimal process window is selected. Process consistency and repeatability is further ensured through process characterization, process qualification, and via quantitative analysis of digital In-Process Quality Metrics™ (IPQM®s).
This paper discusses the rapid qualification methodology, model validation, and the application of the framework towards manufacturing of a challenging part defined by AFRL. The combination of numerical predictions, experimental refinement, and in-process monitoring delivered the first print right at first trial. Distortions are within predictions, geometric accuracy is within expectations, and quantitative metallurgical analysis shows dense as-built material with properties expected to fulfill performance requirements. In-process monitoring results provide a quantitative, digital Quality Signature™ or Digital Quality Record™ of process consistency and product quality.</description><subject>Additive Manufacturing Benchmarks 2018</subject><subject>Characterization and Evaluation of Materials</subject><subject>Chemistry and Materials Science</subject><subject>Consistency</subject><subject>Construction materials</subject><subject>Empirical analysis</subject><subject>Geometric accuracy</subject><subject>Manufacturability</subject><subject>Manufacturing</subject><subject>Materials Science</subject><subject>Mathematical models</subject><subject>Metallic Materials</subject><subject>Metallurgical analysis</subject><subject>Monitoring</subject><subject>Nanotechnology</subject><subject>Powder beds</subject><subject>Process parameters</subject><subject>Quality</subject><subject>Quantitative analysis</subject><subject>Structural Materials</subject><subject>Surfaces and Interfaces</subject><subject>Thematic Section: Additive Manufacturing Benchmarks 2018</subject><subject>Thin Films</subject><issn>2193-9764</issn><issn>2193-9772</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kM1OwzAMxyMEEtPYC3CKxDmQOG2THNm08aFNIDTOUT_crdNoR9KJceMheEKehIwiuO1iW_bvb8t_Qs4FvxScqysfcWGAhcA4F1IyfUR6IIxkRik4_quT6JQMvF_xPRWJRIseuZ8-Dif0qVosW9oukU4q51s6r17w6-Nz3-hGs2pHh9i-IdZ01hS4ruoFTeuCjncbdIGuW39GTsp07XHwm_vkeTKej27Z9OHmbnQ9ZTkkoBlIkCqWWSJNrnkUFyaPRJyhKXRWAvLElKpQqDkHhSbSWaiyoiySPNcpYCb75KLbu3HN6xZ9a1fN1tXhpAWIAGIulDxMCQHcxEYECjoqd433Dku7Cd-k7t0Kbvfm2s5cG4L9MdfqIJKdyAe4XqD7X31A9Q2VRHpO</recordid><startdate>20190615</startdate><enddate>20190615</enddate><creator>Megahed, Mustafa</creator><creator>Mindt, Hans-Wilfried</creator><creator>Willems, Jöerg</creator><creator>Dionne, Paul</creator><creator>Jacquemetton, Lars</creator><creator>Craig, James</creator><creator>Ranade, Piyush</creator><creator>Peralta, Alonso</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0003-3880-6483</orcidid></search><sort><creationdate>20190615</creationdate><title>LPBF Right the First Time—the Right Mix Between Modeling and Experiments</title><author>Megahed, Mustafa ; Mindt, Hans-Wilfried ; Willems, Jöerg ; Dionne, Paul ; Jacquemetton, Lars ; Craig, James ; Ranade, Piyush ; Peralta, Alonso</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2628-2323753b639c8045d9c415be9d8bf2e069f7d7e80027e948b800bdfd6cc8a2eb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Additive Manufacturing Benchmarks 2018</topic><topic>Characterization and Evaluation of Materials</topic><topic>Chemistry and Materials Science</topic><topic>Consistency</topic><topic>Construction materials</topic><topic>Empirical analysis</topic><topic>Geometric accuracy</topic><topic>Manufacturability</topic><topic>Manufacturing</topic><topic>Materials Science</topic><topic>Mathematical models</topic><topic>Metallic Materials</topic><topic>Metallurgical analysis</topic><topic>Monitoring</topic><topic>Nanotechnology</topic><topic>Powder beds</topic><topic>Process parameters</topic><topic>Quality</topic><topic>Quantitative analysis</topic><topic>Structural Materials</topic><topic>Surfaces and Interfaces</topic><topic>Thematic Section: Additive Manufacturing Benchmarks 2018</topic><topic>Thin Films</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Megahed, Mustafa</creatorcontrib><creatorcontrib>Mindt, Hans-Wilfried</creatorcontrib><creatorcontrib>Willems, Jöerg</creatorcontrib><creatorcontrib>Dionne, Paul</creatorcontrib><creatorcontrib>Jacquemetton, Lars</creatorcontrib><creatorcontrib>Craig, James</creatorcontrib><creatorcontrib>Ranade, Piyush</creatorcontrib><creatorcontrib>Peralta, Alonso</creatorcontrib><collection>CrossRef</collection><jtitle>Integrating materials and manufacturing innovation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Megahed, Mustafa</au><au>Mindt, Hans-Wilfried</au><au>Willems, Jöerg</au><au>Dionne, Paul</au><au>Jacquemetton, Lars</au><au>Craig, James</au><au>Ranade, Piyush</au><au>Peralta, Alonso</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>LPBF Right the First Time—the Right Mix Between Modeling and Experiments</atitle><jtitle>Integrating materials and manufacturing innovation</jtitle><stitle>Integr Mater Manuf Innov</stitle><date>2019-06-15</date><risdate>2019</risdate><volume>8</volume><issue>2</issue><spage>194</spage><epage>216</epage><pages>194-216</pages><issn>2193-9764</issn><eissn>2193-9772</eissn><abstract>Laser powder bed fusion (LPBF) is an additive manufacturing process with many adjustable input parameters that directly affect manufacturability and quality of the final product. The selection of the optimal input parameters makes the process qualification and part certification a costly and time-consuming task if performed using the traditional sequential and empirical approach.
Within the scope of the DARPA open manufacturing program, a rapid qualification framework is developed that relies on parallel multi-physics modeling and experimental efforts for verification and validation of the process input parameters during process development and material characterization. Product manufacturability is tested a priori via modeling and in-process monitoring is deployed to ensure input parameters are rapidly screened, and an optimal process window is selected. Process consistency and repeatability is further ensured through process characterization, process qualification, and via quantitative analysis of digital In-Process Quality Metrics™ (IPQM®s).
This paper discusses the rapid qualification methodology, model validation, and the application of the framework towards manufacturing of a challenging part defined by AFRL. The combination of numerical predictions, experimental refinement, and in-process monitoring delivered the first print right at first trial. Distortions are within predictions, geometric accuracy is within expectations, and quantitative metallurgical analysis shows dense as-built material with properties expected to fulfill performance requirements. In-process monitoring results provide a quantitative, digital Quality Signature™ or Digital Quality Record™ of process consistency and product quality.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s40192-019-00133-8</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0003-3880-6483</orcidid></addata></record> |
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subjects | Additive Manufacturing Benchmarks 2018 Characterization and Evaluation of Materials Chemistry and Materials Science Consistency Construction materials Empirical analysis Geometric accuracy Manufacturability Manufacturing Materials Science Mathematical models Metallic Materials Metallurgical analysis Monitoring Nanotechnology Powder beds Process parameters Quality Quantitative analysis Structural Materials Surfaces and Interfaces Thematic Section: Additive Manufacturing Benchmarks 2018 Thin Films |
title | LPBF Right the First Time—the Right Mix Between Modeling and Experiments |
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