Data-driven Virtual Test-bed of the Blown Powder Directed Energy Deposition Process

Digital twins in manufacturing serve as a crucial bridge between the industrial age and the digital age, offering immense value. Current additive manufacturing processes are able to generate vast amounts of in-process data, which, when effectively ingested, can be transformed into insightful decisio...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-09
Main Authors: Juhasz, Michael, Chin, Eric, Choi, Youngsoo, McKeown, Joseph T, Khairallah, Saad
Format: Article
Language:English
Subjects:
Beds (process engineering)
Coupled modes
Deposition
Digital twins
Manufacturing
Robust control
System identification
Virtual reality
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Juhasz, Michael
Chin, Eric
Choi, Youngsoo
McKeown, Joseph T
Khairallah, Saad
description Digital twins in manufacturing serve as a crucial bridge between the industrial age and the digital age, offering immense value. Current additive manufacturing processes are able to generate vast amounts of in-process data, which, when effectively ingested, can be transformed into insightful decisions. Data-driven methods from reduced order modeling and system identification are particularly promising in managing this data deluge. This study focuses on Laser Powder Directed Energy Deposition (LP-DED) equipped with in-situ process measurements to develop a compact virtual test-bed. This test-bed can accurately ingest arbitrary process inputs and report in-process observables as outputs. This virtual test-bed is derived using Dynamic Mode Decomposition with Control (DMDc) and is coupled with uncertainty quantification techniques to ensure robust predictions.
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_3106244001</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3106244001</sourcerecordid><originalsourceid>FETCH-proquest_journals_31062440013</originalsourceid><addsrcrecordid>eNqNjEELgjAYQEcQJOV_-KDzYG5qnUujY5B0laWfNZHNtpn079uhH9DpHd7jLUjEhUjoPuV8RWLnesYYz3c8y0REroX0krZWvVHDTVk_yQEqdJ7esQXTgX8iHAYza7iYuUULhbLY-CBLjfbxgQJH45RXJhTWNOjchiw7OTiMf1yT7amsjmc6WvOawrvuzWR1ULVIWM7TlLFE_Fd9Adp5P2Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3106244001</pqid></control><display><type>article</type><title>Data-driven Virtual Test-bed of the Blown Powder Directed Energy Deposition Process</title><source>Publicly Available Content (ProQuest)</source><creator>Juhasz, Michael ; Chin, Eric ; Choi, Youngsoo ; McKeown, Joseph T ; Khairallah, Saad</creator><creatorcontrib>Juhasz, Michael ; Chin, Eric ; Choi, Youngsoo ; McKeown, Joseph T ; Khairallah, Saad</creatorcontrib><description>Digital twins in manufacturing serve as a crucial bridge between the industrial age and the digital age, offering immense value. Current additive manufacturing processes are able to generate vast amounts of in-process data, which, when effectively ingested, can be transformed into insightful decisions. Data-driven methods from reduced order modeling and system identification are particularly promising in managing this data deluge. This study focuses on Laser Powder Directed Energy Deposition (LP-DED) equipped with in-situ process measurements to develop a compact virtual test-bed. This test-bed can accurately ingest arbitrary process inputs and report in-process observables as outputs. This virtual test-bed is derived using Dynamic Mode Decomposition with Control (DMDc) and is coupled with uncertainty quantification techniques to ensure robust predictions.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Beds (process engineering) ; Coupled modes ; Deposition ; Digital twins ; Manufacturing ; Robust control ; System identification ; Virtual reality</subject><ispartof>arXiv.org, 2024-09</ispartof><rights>2024. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/3106244001?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>777,781,25734,36993,44571</link.rule.ids></links><search><creatorcontrib>Juhasz, Michael</creatorcontrib><creatorcontrib>Chin, Eric</creatorcontrib><creatorcontrib>Choi, Youngsoo</creatorcontrib><creatorcontrib>McKeown, Joseph T</creatorcontrib><creatorcontrib>Khairallah, Saad</creatorcontrib><title>Data-driven Virtual Test-bed of the Blown Powder Directed Energy Deposition Process</title><title>arXiv.org</title><description>Digital twins in manufacturing serve as a crucial bridge between the industrial age and the digital age, offering immense value. Current additive manufacturing processes are able to generate vast amounts of in-process data, which, when effectively ingested, can be transformed into insightful decisions. Data-driven methods from reduced order modeling and system identification are particularly promising in managing this data deluge. This study focuses on Laser Powder Directed Energy Deposition (LP-DED) equipped with in-situ process measurements to develop a compact virtual test-bed. This test-bed can accurately ingest arbitrary process inputs and report in-process observables as outputs. This virtual test-bed is derived using Dynamic Mode Decomposition with Control (DMDc) and is coupled with uncertainty quantification techniques to ensure robust predictions.</description><subject>Beds (process engineering)</subject><subject>Coupled modes</subject><subject>Deposition</subject><subject>Digital twins</subject><subject>Manufacturing</subject><subject>Robust control</subject><subject>System identification</subject><subject>Virtual reality</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqNjEELgjAYQEcQJOV_-KDzYG5qnUujY5B0laWfNZHNtpn079uhH9DpHd7jLUjEhUjoPuV8RWLnesYYz3c8y0REroX0krZWvVHDTVk_yQEqdJ7esQXTgX8iHAYza7iYuUULhbLY-CBLjfbxgQJH45RXJhTWNOjchiw7OTiMf1yT7amsjmc6WvOawrvuzWR1ULVIWM7TlLFE_Fd9Adp5P2Q</recordid><startdate>20240912</startdate><enddate>20240912</enddate><creator>Juhasz, Michael</creator><creator>Chin, Eric</creator><creator>Choi, Youngsoo</creator><creator>McKeown, Joseph T</creator><creator>Khairallah, Saad</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20240912</creationdate><title>Data-driven Virtual Test-bed of the Blown Powder Directed Energy Deposition Process</title><author>Juhasz, Michael ; Chin, Eric ; Choi, Youngsoo ; McKeown, Joseph T ; Khairallah, Saad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_31062440013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Beds (process engineering)</topic><topic>Coupled modes</topic><topic>Deposition</topic><topic>Digital twins</topic><topic>Manufacturing</topic><topic>Robust control</topic><topic>System identification</topic><topic>Virtual reality</topic><toplevel>online_resources</toplevel><creatorcontrib>Juhasz, Michael</creatorcontrib><creatorcontrib>Chin, Eric</creatorcontrib><creatorcontrib>Choi, Youngsoo</creatorcontrib><creatorcontrib>McKeown, Joseph T</creatorcontrib><creatorcontrib>Khairallah, Saad</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Juhasz, Michael</au><au>Chin, Eric</au><au>Choi, Youngsoo</au><au>McKeown, Joseph T</au><au>Khairallah, Saad</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Data-driven Virtual Test-bed of the Blown Powder Directed Energy Deposition Process</atitle><jtitle>arXiv.org</jtitle><date>2024-09-12</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>Digital twins in manufacturing serve as a crucial bridge between the industrial age and the digital age, offering immense value. Current additive manufacturing processes are able to generate vast amounts of in-process data, which, when effectively ingested, can be transformed into insightful decisions. Data-driven methods from reduced order modeling and system identification are particularly promising in managing this data deluge. This study focuses on Laser Powder Directed Energy Deposition (LP-DED) equipped with in-situ process measurements to develop a compact virtual test-bed. This test-bed can accurately ingest arbitrary process inputs and report in-process observables as outputs. This virtual test-bed is derived using Dynamic Mode Decomposition with Control (DMDc) and is coupled with uncertainty quantification techniques to ensure robust predictions.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2024-09
issn 2331-8422
language eng
recordid cdi_proquest_journals_3106244001
source Publicly Available Content (ProQuest)
subjects Beds (process engineering)
Coupled modes
Deposition
Digital twins
Manufacturing
Robust control
System identification
Virtual reality
title Data-driven Virtual Test-bed of the Blown Powder Directed Energy Deposition Process
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-20T02%3A59%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Data-driven%20Virtual%20Test-bed%20of%20the%20Blown%20Powder%20Directed%20Energy%20Deposition%20Process&rft.jtitle=arXiv.org&rft.au=Juhasz,%20Michael&rft.date=2024-09-12&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E3106244001%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_31062440013%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3106244001&rft_id=info:pmid/&rfr_iscdi=true