Neural network-based control for the fiber placement composite manufacturing process
Abstract At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide perfor...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lichtenwalner, P. F. [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
1993 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© ASM International 1993 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Journal of materials engineering and performance - Springer-Verlag, 1992, 2(1993), 5 vom: Okt., Seite 687-691 |
|---|---|
| Übergeordnetes Werk: |
volume:2 ; year:1993 ; number:5 ; month:10 ; pages:687-691 |
| Links: |
|---|
| DOI / URN: |
10.1007/BF02650058 |
|---|
| Katalog-ID: |
OLC205301217X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC205301217X | ||
| 003 | DE-627 | ||
| 005 | 20230401130908.0 | ||
| 007 | tu | ||
| 008 | 200820s1993 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/BF02650058 |2 doi | |
| 035 | |a (DE-627)OLC205301217X | ||
| 035 | |a (DE-He213)BF02650058-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 620 |a 660 |a 670 |q VZ |
| 100 | 1 | |a Lichtenwalner, P. F. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Neural network-based control for the fiber placement composite manufacturing process |
| 264 | 1 | |c 1993 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © ASM International 1993 | ||
| 520 | |a Abstract At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine. | ||
| 650 | 4 | |a Adoptive control | |
| 650 | 4 | |a CMAC | |
| 650 | 4 | |a neural network | |
| 650 | 4 | |a neural control | |
| 650 | 4 | |a online learning | |
| 650 | 4 | |a process contral | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of materials engineering and performance |d Springer-Verlag, 1992 |g 2(1993), 5 vom: Okt., Seite 687-691 |w (DE-627)131147366 |w (DE-600)1129075-4 |w (DE-576)033027250 |x 1059-9495 |7 nnns |
| 773 | 1 | 8 | |g volume:2 |g year:1993 |g number:5 |g month:10 |g pages:687-691 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/BF02650058 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a GBV_ILN_11 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_2006 | ||
| 912 | |a GBV_ILN_2027 | ||
| 912 | |a GBV_ILN_4036 | ||
| 951 | |a AR | ||
| 952 | |d 2 |j 1993 |e 5 |c 10 |h 687-691 | ||
| author_variant |
p f l pf pfl |
|---|---|
| matchkey_str |
article:10599495:1993----::erlewrbsdotofrhfbrlcmncmoie |
| hierarchy_sort_str |
1993 |
| publishDate |
1993 |
| allfields |
10.1007/BF02650058 doi (DE-627)OLC205301217X (DE-He213)BF02650058-p DE-627 ger DE-627 rakwb eng 620 660 670 VZ Lichtenwalner, P. F. verfasserin aut Neural network-based control for the fiber placement composite manufacturing process 1993 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 1993 Abstract At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine. Adoptive control CMAC neural network neural control online learning process contral Enthalten in Journal of materials engineering and performance Springer-Verlag, 1992 2(1993), 5 vom: Okt., Seite 687-691 (DE-627)131147366 (DE-600)1129075-4 (DE-576)033027250 1059-9495 nnns volume:2 year:1993 number:5 month:10 pages:687-691 https://doi.org/10.1007/BF02650058 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_11 GBV_ILN_23 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2027 GBV_ILN_4036 AR 2 1993 5 10 687-691 |
| spelling |
10.1007/BF02650058 doi (DE-627)OLC205301217X (DE-He213)BF02650058-p DE-627 ger DE-627 rakwb eng 620 660 670 VZ Lichtenwalner, P. F. verfasserin aut Neural network-based control for the fiber placement composite manufacturing process 1993 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 1993 Abstract At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine. Adoptive control CMAC neural network neural control online learning process contral Enthalten in Journal of materials engineering and performance Springer-Verlag, 1992 2(1993), 5 vom: Okt., Seite 687-691 (DE-627)131147366 (DE-600)1129075-4 (DE-576)033027250 1059-9495 nnns volume:2 year:1993 number:5 month:10 pages:687-691 https://doi.org/10.1007/BF02650058 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_11 GBV_ILN_23 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2027 GBV_ILN_4036 AR 2 1993 5 10 687-691 |
| allfields_unstemmed |
10.1007/BF02650058 doi (DE-627)OLC205301217X (DE-He213)BF02650058-p DE-627 ger DE-627 rakwb eng 620 660 670 VZ Lichtenwalner, P. F. verfasserin aut Neural network-based control for the fiber placement composite manufacturing process 1993 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 1993 Abstract At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine. Adoptive control CMAC neural network neural control online learning process contral Enthalten in Journal of materials engineering and performance Springer-Verlag, 1992 2(1993), 5 vom: Okt., Seite 687-691 (DE-627)131147366 (DE-600)1129075-4 (DE-576)033027250 1059-9495 nnns volume:2 year:1993 number:5 month:10 pages:687-691 https://doi.org/10.1007/BF02650058 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_11 GBV_ILN_23 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2027 GBV_ILN_4036 AR 2 1993 5 10 687-691 |
| allfieldsGer |
10.1007/BF02650058 doi (DE-627)OLC205301217X (DE-He213)BF02650058-p DE-627 ger DE-627 rakwb eng 620 660 670 VZ Lichtenwalner, P. F. verfasserin aut Neural network-based control for the fiber placement composite manufacturing process 1993 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 1993 Abstract At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine. Adoptive control CMAC neural network neural control online learning process contral Enthalten in Journal of materials engineering and performance Springer-Verlag, 1992 2(1993), 5 vom: Okt., Seite 687-691 (DE-627)131147366 (DE-600)1129075-4 (DE-576)033027250 1059-9495 nnns volume:2 year:1993 number:5 month:10 pages:687-691 https://doi.org/10.1007/BF02650058 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_11 GBV_ILN_23 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2027 GBV_ILN_4036 AR 2 1993 5 10 687-691 |
| allfieldsSound |
10.1007/BF02650058 doi (DE-627)OLC205301217X (DE-He213)BF02650058-p DE-627 ger DE-627 rakwb eng 620 660 670 VZ Lichtenwalner, P. F. verfasserin aut Neural network-based control for the fiber placement composite manufacturing process 1993 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © ASM International 1993 Abstract At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine. Adoptive control CMAC neural network neural control online learning process contral Enthalten in Journal of materials engineering and performance Springer-Verlag, 1992 2(1993), 5 vom: Okt., Seite 687-691 (DE-627)131147366 (DE-600)1129075-4 (DE-576)033027250 1059-9495 nnns volume:2 year:1993 number:5 month:10 pages:687-691 https://doi.org/10.1007/BF02650058 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_11 GBV_ILN_23 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2027 GBV_ILN_4036 AR 2 1993 5 10 687-691 |
| language |
English |
| source |
Enthalten in Journal of materials engineering and performance 2(1993), 5 vom: Okt., Seite 687-691 volume:2 year:1993 number:5 month:10 pages:687-691 |
| sourceStr |
Enthalten in Journal of materials engineering and performance 2(1993), 5 vom: Okt., Seite 687-691 volume:2 year:1993 number:5 month:10 pages:687-691 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Adoptive control CMAC neural network neural control online learning process contral |
| dewey-raw |
620 |
| isfreeaccess_bool |
false |
| container_title |
Journal of materials engineering and performance |
| authorswithroles_txt_mv |
Lichtenwalner, P. F. @@aut@@ |
| publishDateDaySort_date |
1993-10-01T00:00:00Z |
| hierarchy_top_id |
131147366 |
| dewey-sort |
3620 |
| id |
OLC205301217X |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC205301217X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230401130908.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s1993 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/BF02650058</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC205301217X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)BF02650058-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="a">660</subfield><subfield code="a">670</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lichtenwalner, P. F.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Neural network-based control for the fiber placement composite manufacturing process</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">1993</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© ASM International 1993</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adoptive control</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CMAC</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neural control</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">online learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">process contral</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of materials engineering and performance</subfield><subfield code="d">Springer-Verlag, 1992</subfield><subfield code="g">2(1993), 5 vom: Okt., Seite 687-691</subfield><subfield code="w">(DE-627)131147366</subfield><subfield code="w">(DE-600)1129075-4</subfield><subfield code="w">(DE-576)033027250</subfield><subfield code="x">1059-9495</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:2</subfield><subfield code="g">year:1993</subfield><subfield code="g">number:5</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:687-691</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/BF02650058</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4036</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">2</subfield><subfield code="j">1993</subfield><subfield code="e">5</subfield><subfield code="c">10</subfield><subfield code="h">687-691</subfield></datafield></record></collection>
|
| author |
Lichtenwalner, P. F. |
| spellingShingle |
Lichtenwalner, P. F. ddc 620 misc Adoptive control misc CMAC misc neural network misc neural control misc online learning misc process contral Neural network-based control for the fiber placement composite manufacturing process |
| authorStr |
Lichtenwalner, P. F. |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)131147366 |
| format |
Article |
| dewey-ones |
620 - Engineering & allied operations 660 - Chemical engineering 670 - Manufacturing |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1059-9495 |
| topic_title |
620 660 670 VZ Neural network-based control for the fiber placement composite manufacturing process Adoptive control CMAC neural network neural control online learning process contral |
| topic |
ddc 620 misc Adoptive control misc CMAC misc neural network misc neural control misc online learning misc process contral |
| topic_unstemmed |
ddc 620 misc Adoptive control misc CMAC misc neural network misc neural control misc online learning misc process contral |
| topic_browse |
ddc 620 misc Adoptive control misc CMAC misc neural network misc neural control misc online learning misc process contral |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Journal of materials engineering and performance |
| hierarchy_parent_id |
131147366 |
| dewey-tens |
620 - Engineering 660 - Chemical engineering 670 - Manufacturing |
| hierarchy_top_title |
Journal of materials engineering and performance |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)131147366 (DE-600)1129075-4 (DE-576)033027250 |
| title |
Neural network-based control for the fiber placement composite manufacturing process |
| ctrlnum |
(DE-627)OLC205301217X (DE-He213)BF02650058-p |
| title_full |
Neural network-based control for the fiber placement composite manufacturing process |
| author_sort |
Lichtenwalner, P. F. |
| journal |
Journal of materials engineering and performance |
| journalStr |
Journal of materials engineering and performance |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
1993 |
| contenttype_str_mv |
txt |
| container_start_page |
687 |
| author_browse |
Lichtenwalner, P. F. |
| container_volume |
2 |
| class |
620 660 670 VZ |
| format_se |
Aufsätze |
| author-letter |
Lichtenwalner, P. F. |
| doi_str_mv |
10.1007/BF02650058 |
| dewey-full |
620 660 670 |
| title_sort |
neural network-based control for the fiber placement composite manufacturing process |
| title_auth |
Neural network-based control for the fiber placement composite manufacturing process |
| abstract |
Abstract At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine. © ASM International 1993 |
| abstractGer |
Abstract At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine. © ASM International 1993 |
| abstract_unstemmed |
Abstract At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine. © ASM International 1993 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_11 GBV_ILN_23 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2027 GBV_ILN_4036 |
| container_issue |
5 |
| title_short |
Neural network-based control for the fiber placement composite manufacturing process |
| url |
https://doi.org/10.1007/BF02650058 |
| remote_bool |
false |
| ppnlink |
131147366 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/BF02650058 |
| up_date |
2024-07-03T17:39:26.145Z |
| _version_ |
1803580464060956672 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC205301217X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230401130908.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s1993 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/BF02650058</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC205301217X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)BF02650058-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="a">660</subfield><subfield code="a">670</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lichtenwalner, P. F.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Neural network-based control for the fiber placement composite manufacturing process</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">1993</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© ASM International 1993</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract At McDonnell Douglas Aerospace (MDA), an artificial neural network-based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns the inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional-integral (PI) controller. However, after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A cerebellar model articulation controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM-compatible 386 PC with an A/D board interface to the machine.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adoptive control</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CMAC</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neural control</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">online learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">process contral</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of materials engineering and performance</subfield><subfield code="d">Springer-Verlag, 1992</subfield><subfield code="g">2(1993), 5 vom: Okt., Seite 687-691</subfield><subfield code="w">(DE-627)131147366</subfield><subfield code="w">(DE-600)1129075-4</subfield><subfield code="w">(DE-576)033027250</subfield><subfield code="x">1059-9495</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:2</subfield><subfield code="g">year:1993</subfield><subfield code="g">number:5</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:687-691</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/BF02650058</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4036</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">2</subfield><subfield code="j">1993</subfield><subfield code="e">5</subfield><subfield code="c">10</subfield><subfield code="h">687-691</subfield></datafield></record></collection>
|
| score |
7.40125 |