Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development
Abstract Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical programming model is presented to pred...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
MA, Songhua [verfasserIn] JIANG, Zhaoliang [verfasserIn] LIU, Wenping [verfasserIn] HUANG, Chuanzhen [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2017 |
|---|
| Schlagwörter: |
Change propagation intensity(CPI) |
|---|
| Übergeordnetes Werk: |
Enthalten in: Chinese Journal of Mechanical Engineering - Chinese Mechanical Engineering Society, 2012, 30(2017), 3 vom: 20. März, Seite 676-688 |
|---|---|
| Übergeordnetes Werk: |
volume:30 ; year:2017 ; number:3 ; day:20 ; month:03 ; pages:676-688 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10033-017-0099-z |
|---|
| Katalog-ID: |
SPR008132135 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR008132135 | ||
| 003 | DE-627 | ||
| 005 | 20201124023452.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 201005s2017 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10033-017-0099-z |2 doi | |
| 035 | |a (DE-627)SPR008132135 | ||
| 035 | |a (SPR)s10033-017-0099-z-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a MA, Songhua |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development |
| 264 | 1 | |c 2017 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical programming model is presented to predict the change propagation impact quantitatively. As the foundation of change propagation prediction, a design change analysis model(DCAM) is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity(CPI) is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimization(ACO) algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The proposed change propagation prediction method is verified to be efficient and effective, which could provide different results according to various the initial changes. | ||
| 650 | 4 | |a Change propagation prediction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Small-world network |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Change propagation intensity(CPI) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Design change analysis model(DCAM) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Ant colony optimization(ACO) |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a JIANG, Zhaoliang |e verfasserin |4 aut | |
| 700 | 1 | |a LIU, Wenping |e verfasserin |4 aut | |
| 700 | 1 | |a HUANG, Chuanzhen |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Chinese Journal of Mechanical Engineering |d Chinese Mechanical Engineering Society, 2012 |g 30(2017), 3 vom: 20. März, Seite 676-688 |w (DE-627)SPR008124000 |7 nnns |
| 773 | 1 | 8 | |g volume:30 |g year:2017 |g number:3 |g day:20 |g month:03 |g pages:676-688 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s10033-017-0099-z |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 951 | |a AR | ||
| 952 | |d 30 |j 2017 |e 3 |b 20 |c 03 |h 676-688 | ||
| author_variant |
s m sm z j zj w l wl c h ch |
|---|---|
| matchkey_str |
masonghuajiangzhaoliangliuwenpinghuangch:2017----:einrpryewrbsdhnerpgtopeitoapocfreh |
| hierarchy_sort_str |
2017 |
| publishDate |
2017 |
| allfields |
10.1007/s10033-017-0099-z doi (DE-627)SPR008132135 (SPR)s10033-017-0099-z-e DE-627 ger DE-627 rakwb eng MA, Songhua verfasserin aut Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical programming model is presented to predict the change propagation impact quantitatively. As the foundation of change propagation prediction, a design change analysis model(DCAM) is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity(CPI) is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimization(ACO) algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The proposed change propagation prediction method is verified to be efficient and effective, which could provide different results according to various the initial changes. Change propagation prediction (dpeaa)DE-He213 Small-world network (dpeaa)DE-He213 Change propagation intensity(CPI) (dpeaa)DE-He213 Design change analysis model(DCAM) (dpeaa)DE-He213 Ant colony optimization(ACO) (dpeaa)DE-He213 JIANG, Zhaoliang verfasserin aut LIU, Wenping verfasserin aut HUANG, Chuanzhen verfasserin aut Enthalten in Chinese Journal of Mechanical Engineering Chinese Mechanical Engineering Society, 2012 30(2017), 3 vom: 20. März, Seite 676-688 (DE-627)SPR008124000 nnns volume:30 year:2017 number:3 day:20 month:03 pages:676-688 https://dx.doi.org/10.1007/s10033-017-0099-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 30 2017 3 20 03 676-688 |
| spelling |
10.1007/s10033-017-0099-z doi (DE-627)SPR008132135 (SPR)s10033-017-0099-z-e DE-627 ger DE-627 rakwb eng MA, Songhua verfasserin aut Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical programming model is presented to predict the change propagation impact quantitatively. As the foundation of change propagation prediction, a design change analysis model(DCAM) is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity(CPI) is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimization(ACO) algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The proposed change propagation prediction method is verified to be efficient and effective, which could provide different results according to various the initial changes. Change propagation prediction (dpeaa)DE-He213 Small-world network (dpeaa)DE-He213 Change propagation intensity(CPI) (dpeaa)DE-He213 Design change analysis model(DCAM) (dpeaa)DE-He213 Ant colony optimization(ACO) (dpeaa)DE-He213 JIANG, Zhaoliang verfasserin aut LIU, Wenping verfasserin aut HUANG, Chuanzhen verfasserin aut Enthalten in Chinese Journal of Mechanical Engineering Chinese Mechanical Engineering Society, 2012 30(2017), 3 vom: 20. März, Seite 676-688 (DE-627)SPR008124000 nnns volume:30 year:2017 number:3 day:20 month:03 pages:676-688 https://dx.doi.org/10.1007/s10033-017-0099-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 30 2017 3 20 03 676-688 |
| allfields_unstemmed |
10.1007/s10033-017-0099-z doi (DE-627)SPR008132135 (SPR)s10033-017-0099-z-e DE-627 ger DE-627 rakwb eng MA, Songhua verfasserin aut Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical programming model is presented to predict the change propagation impact quantitatively. As the foundation of change propagation prediction, a design change analysis model(DCAM) is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity(CPI) is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimization(ACO) algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The proposed change propagation prediction method is verified to be efficient and effective, which could provide different results according to various the initial changes. Change propagation prediction (dpeaa)DE-He213 Small-world network (dpeaa)DE-He213 Change propagation intensity(CPI) (dpeaa)DE-He213 Design change analysis model(DCAM) (dpeaa)DE-He213 Ant colony optimization(ACO) (dpeaa)DE-He213 JIANG, Zhaoliang verfasserin aut LIU, Wenping verfasserin aut HUANG, Chuanzhen verfasserin aut Enthalten in Chinese Journal of Mechanical Engineering Chinese Mechanical Engineering Society, 2012 30(2017), 3 vom: 20. März, Seite 676-688 (DE-627)SPR008124000 nnns volume:30 year:2017 number:3 day:20 month:03 pages:676-688 https://dx.doi.org/10.1007/s10033-017-0099-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 30 2017 3 20 03 676-688 |
| allfieldsGer |
10.1007/s10033-017-0099-z doi (DE-627)SPR008132135 (SPR)s10033-017-0099-z-e DE-627 ger DE-627 rakwb eng MA, Songhua verfasserin aut Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical programming model is presented to predict the change propagation impact quantitatively. As the foundation of change propagation prediction, a design change analysis model(DCAM) is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity(CPI) is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimization(ACO) algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The proposed change propagation prediction method is verified to be efficient and effective, which could provide different results according to various the initial changes. Change propagation prediction (dpeaa)DE-He213 Small-world network (dpeaa)DE-He213 Change propagation intensity(CPI) (dpeaa)DE-He213 Design change analysis model(DCAM) (dpeaa)DE-He213 Ant colony optimization(ACO) (dpeaa)DE-He213 JIANG, Zhaoliang verfasserin aut LIU, Wenping verfasserin aut HUANG, Chuanzhen verfasserin aut Enthalten in Chinese Journal of Mechanical Engineering Chinese Mechanical Engineering Society, 2012 30(2017), 3 vom: 20. März, Seite 676-688 (DE-627)SPR008124000 nnns volume:30 year:2017 number:3 day:20 month:03 pages:676-688 https://dx.doi.org/10.1007/s10033-017-0099-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 30 2017 3 20 03 676-688 |
| allfieldsSound |
10.1007/s10033-017-0099-z doi (DE-627)SPR008132135 (SPR)s10033-017-0099-z-e DE-627 ger DE-627 rakwb eng MA, Songhua verfasserin aut Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical programming model is presented to predict the change propagation impact quantitatively. As the foundation of change propagation prediction, a design change analysis model(DCAM) is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity(CPI) is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimization(ACO) algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The proposed change propagation prediction method is verified to be efficient and effective, which could provide different results according to various the initial changes. Change propagation prediction (dpeaa)DE-He213 Small-world network (dpeaa)DE-He213 Change propagation intensity(CPI) (dpeaa)DE-He213 Design change analysis model(DCAM) (dpeaa)DE-He213 Ant colony optimization(ACO) (dpeaa)DE-He213 JIANG, Zhaoliang verfasserin aut LIU, Wenping verfasserin aut HUANG, Chuanzhen verfasserin aut Enthalten in Chinese Journal of Mechanical Engineering Chinese Mechanical Engineering Society, 2012 30(2017), 3 vom: 20. März, Seite 676-688 (DE-627)SPR008124000 nnns volume:30 year:2017 number:3 day:20 month:03 pages:676-688 https://dx.doi.org/10.1007/s10033-017-0099-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 30 2017 3 20 03 676-688 |
| language |
English |
| source |
Enthalten in Chinese Journal of Mechanical Engineering 30(2017), 3 vom: 20. März, Seite 676-688 volume:30 year:2017 number:3 day:20 month:03 pages:676-688 |
| sourceStr |
Enthalten in Chinese Journal of Mechanical Engineering 30(2017), 3 vom: 20. März, Seite 676-688 volume:30 year:2017 number:3 day:20 month:03 pages:676-688 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Change propagation prediction Small-world network Change propagation intensity(CPI) Design change analysis model(DCAM) Ant colony optimization(ACO) |
| isfreeaccess_bool |
false |
| container_title |
Chinese Journal of Mechanical Engineering |
| authorswithroles_txt_mv |
MA, Songhua @@aut@@ JIANG, Zhaoliang @@aut@@ LIU, Wenping @@aut@@ HUANG, Chuanzhen @@aut@@ |
| publishDateDaySort_date |
2017-03-20T00:00:00Z |
| hierarchy_top_id |
SPR008124000 |
| id |
SPR008132135 |
| 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">SPR008132135</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124023452.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10033-017-0099-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR008132135</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10033-017-0099-z-e</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="100" ind1="1" ind2=" "><subfield code="a">MA, Songhua</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical programming model is presented to predict the change propagation impact quantitatively. As the foundation of change propagation prediction, a design change analysis model(DCAM) is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity(CPI) is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimization(ACO) algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The proposed change propagation prediction method is verified to be efficient and effective, which could provide different results according to various the initial changes.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Change propagation prediction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Small-world network</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Change propagation intensity(CPI)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Design change analysis model(DCAM)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ant colony optimization(ACO)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">JIANG, Zhaoliang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">LIU, Wenping</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">HUANG, Chuanzhen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Chinese Journal of Mechanical Engineering</subfield><subfield code="d">Chinese Mechanical Engineering Society, 2012</subfield><subfield code="g">30(2017), 3 vom: 20. März, Seite 676-688</subfield><subfield code="w">(DE-627)SPR008124000</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:30</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:3</subfield><subfield code="g">day:20</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:676-688</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10033-017-0099-z</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_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">30</subfield><subfield code="j">2017</subfield><subfield code="e">3</subfield><subfield code="b">20</subfield><subfield code="c">03</subfield><subfield code="h">676-688</subfield></datafield></record></collection>
|
| author |
MA, Songhua |
| spellingShingle |
MA, Songhua misc Change propagation prediction misc Small-world network misc Change propagation intensity(CPI) misc Design change analysis model(DCAM) misc Ant colony optimization(ACO) Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development |
| authorStr |
MA, Songhua |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)SPR008124000 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development Change propagation prediction (dpeaa)DE-He213 Small-world network (dpeaa)DE-He213 Change propagation intensity(CPI) (dpeaa)DE-He213 Design change analysis model(DCAM) (dpeaa)DE-He213 Ant colony optimization(ACO) (dpeaa)DE-He213 |
| topic |
misc Change propagation prediction misc Small-world network misc Change propagation intensity(CPI) misc Design change analysis model(DCAM) misc Ant colony optimization(ACO) |
| topic_unstemmed |
misc Change propagation prediction misc Small-world network misc Change propagation intensity(CPI) misc Design change analysis model(DCAM) misc Ant colony optimization(ACO) |
| topic_browse |
misc Change propagation prediction misc Small-world network misc Change propagation intensity(CPI) misc Design change analysis model(DCAM) misc Ant colony optimization(ACO) |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Chinese Journal of Mechanical Engineering |
| hierarchy_parent_id |
SPR008124000 |
| hierarchy_top_title |
Chinese Journal of Mechanical Engineering |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)SPR008124000 |
| title |
Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development |
| ctrlnum |
(DE-627)SPR008132135 (SPR)s10033-017-0099-z-e |
| title_full |
Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development |
| author_sort |
MA, Songhua |
| journal |
Chinese Journal of Mechanical Engineering |
| journalStr |
Chinese Journal of Mechanical Engineering |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2017 |
| contenttype_str_mv |
txt |
| container_start_page |
676 |
| author_browse |
MA, Songhua JIANG, Zhaoliang LIU, Wenping HUANG, Chuanzhen |
| container_volume |
30 |
| format_se |
Elektronische Aufsätze |
| author-letter |
MA, Songhua |
| doi_str_mv |
10.1007/s10033-017-0099-z |
| author2-role |
verfasserin |
| title_sort |
design property network-based change propagation prediction approach for mechanical product development |
| title_auth |
Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development |
| abstract |
Abstract Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical programming model is presented to predict the change propagation impact quantitatively. As the foundation of change propagation prediction, a design change analysis model(DCAM) is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity(CPI) is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimization(ACO) algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The proposed change propagation prediction method is verified to be efficient and effective, which could provide different results according to various the initial changes. |
| abstractGer |
Abstract Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical programming model is presented to predict the change propagation impact quantitatively. As the foundation of change propagation prediction, a design change analysis model(DCAM) is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity(CPI) is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimization(ACO) algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The proposed change propagation prediction method is verified to be efficient and effective, which could provide different results according to various the initial changes. |
| abstract_unstemmed |
Abstract Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical programming model is presented to predict the change propagation impact quantitatively. As the foundation of change propagation prediction, a design change analysis model(DCAM) is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity(CPI) is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimization(ACO) algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The proposed change propagation prediction method is verified to be efficient and effective, which could provide different results according to various the initial changes. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER |
| container_issue |
3 |
| title_short |
Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development |
| url |
https://dx.doi.org/10.1007/s10033-017-0099-z |
| remote_bool |
true |
| author2 |
JIANG, Zhaoliang LIU, Wenping HUANG, Chuanzhen |
| author2Str |
JIANG, Zhaoliang LIU, Wenping HUANG, Chuanzhen |
| ppnlink |
SPR008124000 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10033-017-0099-z |
| up_date |
2024-07-03T17:31:00.272Z |
| _version_ |
1803579933616766976 |
| 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">SPR008132135</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124023452.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10033-017-0099-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR008132135</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10033-017-0099-z-e</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="100" ind1="1" ind2=" "><subfield code="a">MA, Songhua</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</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">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical programming model is presented to predict the change propagation impact quantitatively. As the foundation of change propagation prediction, a design change analysis model(DCAM) is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity(CPI) is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimization(ACO) algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The proposed change propagation prediction method is verified to be efficient and effective, which could provide different results according to various the initial changes.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Change propagation prediction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Small-world network</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Change propagation intensity(CPI)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Design change analysis model(DCAM)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ant colony optimization(ACO)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">JIANG, Zhaoliang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">LIU, Wenping</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">HUANG, Chuanzhen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Chinese Journal of Mechanical Engineering</subfield><subfield code="d">Chinese Mechanical Engineering Society, 2012</subfield><subfield code="g">30(2017), 3 vom: 20. März, Seite 676-688</subfield><subfield code="w">(DE-627)SPR008124000</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:30</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:3</subfield><subfield code="g">day:20</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:676-688</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s10033-017-0099-z</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_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">30</subfield><subfield code="j">2017</subfield><subfield code="e">3</subfield><subfield code="b">20</subfield><subfield code="c">03</subfield><subfield code="h">676-688</subfield></datafield></record></collection>
|
| score |
7.3996086 |