Drop-on-Demand Inkjet Drop Control With One-Step Look Ahead Estimation of Model Parameters
Applications of drop-on-demand inkjet printing in dosage-matter manufacturing and scalable patterning are attributed to its capacity for producing consistent dosages with high placement accuracy. In practice, with the same drop jetting profile, drop volume and drop jetting velocity are affected by v...
Gespeichert in:
| Veröffentlicht in: | IEEE/ASME transactions on mechatronics 2023-08, Vol.28 (4), p.1-10 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 10 |
|---|---|
| container_issue | 4 |
| container_start_page | 1 |
| container_title | IEEE/ASME transactions on mechatronics |
| container_volume | 28 |
| creator | Wang, Jie Chiu, George T.-C. |
| description | Applications of drop-on-demand inkjet printing in dosage-matter manufacturing and scalable patterning are attributed to its capacity for producing consistent dosages with high placement accuracy. In practice, with the same drop jetting profile, drop volume and drop jetting velocity are affected by variations in ink properties and environmental conditions. Open-loop calibrations are time-consuming and contribute to frequent line stoppage or unacceptable product variations. In this work, a two-input two-output stochastic drop volume and jetting velocity model is derived based on ink jetting calibration data. A control algorithm using drop-image-based one-step look ahead estimation of process model parameters is developed to regulate drop volume and jetting velocity. Boundedness and convergence of the parameter estimation error and stability of the closed-loop system are provided. Experimental results demonstrate a significant reduction to within 1% relative error in the drop volume and jetting velocity using the proposed control algorithm. |
| doi_str_mv | 10.1109/TMECH.2023.3277455 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_10153772</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10153772</ieee_id><sourcerecordid>2851333699</sourcerecordid><originalsourceid>FETCH-LOGICAL-c296t-713cbbe92f480981f4bd6ec1e403c13a84d4503064d5500445478665017bd3a83</originalsourceid><addsrcrecordid>eNpNkElLA0EQhRtRMEb_gHho8Nyxq5dZjmESTSAhghHFSzNLDZks07G7c_DfOzE5eKqi6r2qx0fIPfABAE-flvNxNhkILuRAijhWWl-QHqQKGAf1edn1PJFMKamvyY33a865Ag498jVyds9sy0a4y9uKTtvNGgM9Tmlm2-Dsln40YUUXLbK3gHs6s3ZDhyvMKzr2odnlobEttTWd2wq39DV3-Q4DOn9Lrup86_HuXPvk_Xm8zCZstniZZsMZK0UaBRaDLIsCU1GrhKcJ1KqoIiwBFZclyDxRldJc8khVWnexlVZxEkWaQ1xU3Vr2yePp7t7Z7wP6YNb24NrupRGJBilllKadSpxUpbPeO6zN3nXh3Y8Bbo4MzR9Dc2Rozgw708PJ1CDiPwNoGcdC_gJVNmtq</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2851333699</pqid></control><display><type>article</type><title>Drop-on-Demand Inkjet Drop Control With One-Step Look Ahead Estimation of Model Parameters</title><source>IEEE Electronic Library (IEL)</source><creator>Wang, Jie ; Chiu, George T.-C.</creator><creatorcontrib>Wang, Jie ; Chiu, George T.-C.</creatorcontrib><description>Applications of drop-on-demand inkjet printing in dosage-matter manufacturing and scalable patterning are attributed to its capacity for producing consistent dosages with high placement accuracy. In practice, with the same drop jetting profile, drop volume and drop jetting velocity are affected by variations in ink properties and environmental conditions. Open-loop calibrations are time-consuming and contribute to frequent line stoppage or unacceptable product variations. In this work, a two-input two-output stochastic drop volume and jetting velocity model is derived based on ink jetting calibration data. A control algorithm using drop-image-based one-step look ahead estimation of process model parameters is developed to regulate drop volume and jetting velocity. Boundedness and convergence of the parameter estimation error and stability of the closed-loop system are provided. Experimental results demonstrate a significant reduction to within 1% relative error in the drop volume and jetting velocity using the proposed control algorithm.</description><identifier>ISSN: 1083-4435</identifier><identifier>EISSN: 1941-014X</identifier><identifier>DOI: 10.1109/TMECH.2023.3277455</identifier><identifier>CODEN: IATEFW</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Additive manufacturing ; Algorithms ; Closed loops ; Control theory ; Data models ; Dosage ; Feedback control ; inkjet ; Inkjet printing ; Kalman filter ; Mathematical models ; Parameter estimation ; process control ; Process parameters ; Solid modeling ; stochastic system ; Tuning ; US Department of Defense</subject><ispartof>IEEE/ASME transactions on mechatronics, 2023-08, Vol.28 (4), p.1-10</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c296t-713cbbe92f480981f4bd6ec1e403c13a84d4503064d5500445478665017bd3a83</citedby><cites>FETCH-LOGICAL-c296t-713cbbe92f480981f4bd6ec1e403c13a84d4503064d5500445478665017bd3a83</cites><orcidid>0000-0002-8830-4425 ; 0000-0002-4445-2821</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10153772$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10153772$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wang, Jie</creatorcontrib><creatorcontrib>Chiu, George T.-C.</creatorcontrib><title>Drop-on-Demand Inkjet Drop Control With One-Step Look Ahead Estimation of Model Parameters</title><title>IEEE/ASME transactions on mechatronics</title><addtitle>TMECH</addtitle><description>Applications of drop-on-demand inkjet printing in dosage-matter manufacturing and scalable patterning are attributed to its capacity for producing consistent dosages with high placement accuracy. In practice, with the same drop jetting profile, drop volume and drop jetting velocity are affected by variations in ink properties and environmental conditions. Open-loop calibrations are time-consuming and contribute to frequent line stoppage or unacceptable product variations. In this work, a two-input two-output stochastic drop volume and jetting velocity model is derived based on ink jetting calibration data. A control algorithm using drop-image-based one-step look ahead estimation of process model parameters is developed to regulate drop volume and jetting velocity. Boundedness and convergence of the parameter estimation error and stability of the closed-loop system are provided. Experimental results demonstrate a significant reduction to within 1% relative error in the drop volume and jetting velocity using the proposed control algorithm.</description><subject>Additive manufacturing</subject><subject>Algorithms</subject><subject>Closed loops</subject><subject>Control theory</subject><subject>Data models</subject><subject>Dosage</subject><subject>Feedback control</subject><subject>inkjet</subject><subject>Inkjet printing</subject><subject>Kalman filter</subject><subject>Mathematical models</subject><subject>Parameter estimation</subject><subject>process control</subject><subject>Process parameters</subject><subject>Solid modeling</subject><subject>stochastic system</subject><subject>Tuning</subject><subject>US Department of Defense</subject><issn>1083-4435</issn><issn>1941-014X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkElLA0EQhRtRMEb_gHho8Nyxq5dZjmESTSAhghHFSzNLDZks07G7c_DfOzE5eKqi6r2qx0fIPfABAE-flvNxNhkILuRAijhWWl-QHqQKGAf1edn1PJFMKamvyY33a865Ag498jVyds9sy0a4y9uKTtvNGgM9Tmlm2-Dsln40YUUXLbK3gHs6s3ZDhyvMKzr2odnlobEttTWd2wq39DV3-Q4DOn9Lrup86_HuXPvk_Xm8zCZstniZZsMZK0UaBRaDLIsCU1GrhKcJ1KqoIiwBFZclyDxRldJc8khVWnexlVZxEkWaQ1xU3Vr2yePp7t7Z7wP6YNb24NrupRGJBilllKadSpxUpbPeO6zN3nXh3Y8Bbo4MzR9Dc2Rozgw708PJ1CDiPwNoGcdC_gJVNmtq</recordid><startdate>20230801</startdate><enddate>20230801</enddate><creator>Wang, Jie</creator><creator>Chiu, George T.-C.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-8830-4425</orcidid><orcidid>https://orcid.org/0000-0002-4445-2821</orcidid></search><sort><creationdate>20230801</creationdate><title>Drop-on-Demand Inkjet Drop Control With One-Step Look Ahead Estimation of Model Parameters</title><author>Wang, Jie ; Chiu, George T.-C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c296t-713cbbe92f480981f4bd6ec1e403c13a84d4503064d5500445478665017bd3a83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Additive manufacturing</topic><topic>Algorithms</topic><topic>Closed loops</topic><topic>Control theory</topic><topic>Data models</topic><topic>Dosage</topic><topic>Feedback control</topic><topic>inkjet</topic><topic>Inkjet printing</topic><topic>Kalman filter</topic><topic>Mathematical models</topic><topic>Parameter estimation</topic><topic>process control</topic><topic>Process parameters</topic><topic>Solid modeling</topic><topic>stochastic system</topic><topic>Tuning</topic><topic>US Department of Defense</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Jie</creatorcontrib><creatorcontrib>Chiu, George T.-C.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE/ASME transactions on mechatronics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Jie</au><au>Chiu, George T.-C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Drop-on-Demand Inkjet Drop Control With One-Step Look Ahead Estimation of Model Parameters</atitle><jtitle>IEEE/ASME transactions on mechatronics</jtitle><stitle>TMECH</stitle><date>2023-08-01</date><risdate>2023</risdate><volume>28</volume><issue>4</issue><spage>1</spage><epage>10</epage><pages>1-10</pages><issn>1083-4435</issn><eissn>1941-014X</eissn><coden>IATEFW</coden><abstract>Applications of drop-on-demand inkjet printing in dosage-matter manufacturing and scalable patterning are attributed to its capacity for producing consistent dosages with high placement accuracy. In practice, with the same drop jetting profile, drop volume and drop jetting velocity are affected by variations in ink properties and environmental conditions. Open-loop calibrations are time-consuming and contribute to frequent line stoppage or unacceptable product variations. In this work, a two-input two-output stochastic drop volume and jetting velocity model is derived based on ink jetting calibration data. A control algorithm using drop-image-based one-step look ahead estimation of process model parameters is developed to regulate drop volume and jetting velocity. Boundedness and convergence of the parameter estimation error and stability of the closed-loop system are provided. Experimental results demonstrate a significant reduction to within 1% relative error in the drop volume and jetting velocity using the proposed control algorithm.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TMECH.2023.3277455</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-8830-4425</orcidid><orcidid>https://orcid.org/0000-0002-4445-2821</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1083-4435 |
| ispartof | IEEE/ASME transactions on mechatronics, 2023-08, Vol.28 (4), p.1-10 |
| issn | 1083-4435 1941-014X |
| language | eng |
| recordid | cdi_ieee_primary_10153772 |
| source | IEEE Electronic Library (IEL) |
| subjects | Additive manufacturing Algorithms Closed loops Control theory Data models Dosage Feedback control inkjet Inkjet printing Kalman filter Mathematical models Parameter estimation process control Process parameters Solid modeling stochastic system Tuning US Department of Defense |
| title | Drop-on-Demand Inkjet Drop Control With One-Step Look Ahead Estimation of Model Parameters |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T00%3A06%3A46IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Drop-on-Demand%20Inkjet%20Drop%20Control%20With%20One-Step%20Look%20Ahead%20Estimation%20of%20Model%20Parameters&rft.jtitle=IEEE/ASME%20transactions%20on%20mechatronics&rft.au=Wang,%20Jie&rft.date=2023-08-01&rft.volume=28&rft.issue=4&rft.spage=1&rft.epage=10&rft.pages=1-10&rft.issn=1083-4435&rft.eissn=1941-014X&rft.coden=IATEFW&rft_id=info:doi/10.1109/TMECH.2023.3277455&rft_dat=%3Cproquest_RIE%3E2851333699%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2851333699&rft_id=info:pmid/&rft_ieee_id=10153772&rfr_iscdi=true |