Part family formation method for delayed reconfigurable manufacturing system based on machine learning
Abstract Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of par...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Huang, Sihan [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Journal of intelligent manufacturing - Springer US, 1990, 34(2022), 6 vom: 14. Juni, Seite 2849-2863 |
|---|---|
| Übergeordnetes Werk: |
volume:34 ; year:2022 ; number:6 ; day:14 ; month:06 ; pages:2849-2863 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10845-022-01956-7 |
|---|
| Katalog-ID: |
OLC2143778708 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2143778708 | ||
| 003 | DE-627 | ||
| 005 | 20240118091713.0 | ||
| 007 | tu | ||
| 008 | 240118s2022 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10845-022-01956-7 |2 doi | |
| 035 | |a (DE-627)OLC2143778708 | ||
| 035 | |a (DE-He213)s10845-022-01956-7-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 620 |a 004 |q VZ |
| 100 | 1 | |a Huang, Sihan |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Part family formation method for delayed reconfigurable manufacturing system based on machine learning |
| 264 | 1 | |c 2022 | |
| 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 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 | ||
| 520 | |a Abstract Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of part family formation. To bring the characteristics of delayed reconfiguration into the part family of D-RMS, an exclusive part family formation method for D-RMS based on machine learning is proposed in this paper. Firstly, a similarity coefficient that considers the characteristics of D-RMS is put forward based on the operation sequence of part. The positions of the common operations in the corresponding operation sequences are investigated. The more former common operations there are, the more probability it is that the parts are grouped into the same part family. The relative positions of the common operations are considered by proposing a concept of the longest relative position common operation subsequence (LPCS). Additionally, the position difference and discontinuity of the LPCSs in the corresponding operation sequences are analyzed. A similarity coefficient is proposed that incorporates the abovementioned factors. Secondly, a machine learning method named K-medoids is adopted to group parts into families based on the calculation result of the similarity coefficient. Finally, a case study is presented to implement the proposed part family formation method for D-RMS, where the effectiveness of the proposed method is verified through comparison. | ||
| 650 | 4 | |a Delayed reconfigurable manufacturing system | |
| 650 | 4 | |a Part family formation | |
| 650 | 4 | |a Similarity coefficient | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a K-medoids | |
| 700 | 1 | |a Wang, Guoxin |0 (orcid)0000-0003-2363-8595 |4 aut | |
| 700 | 1 | |a Nie, Shiqi |4 aut | |
| 700 | 1 | |a Wang, Bin |4 aut | |
| 700 | 1 | |a Yan, Yan |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of intelligent manufacturing |d Springer US, 1990 |g 34(2022), 6 vom: 14. Juni, Seite 2849-2863 |w (DE-627)130892815 |w (DE-600)1041378-9 |w (DE-576)026321106 |x 0956-5515 |7 nnns |
| 773 | 1 | 8 | |g volume:34 |g year:2022 |g number:6 |g day:14 |g month:06 |g pages:2849-2863 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s10845-022-01956-7 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a SSG-OLC-MAT | ||
| 951 | |a AR | ||
| 952 | |d 34 |j 2022 |e 6 |b 14 |c 06 |h 2849-2863 | ||
| author_variant |
s h sh g w gw s n sn b w bw y y yy |
|---|---|
| matchkey_str |
article:09565515:2022----::ataiyomtomtofreaercniualmnfcuigyt |
| hierarchy_sort_str |
2022 |
| publishDate |
2022 |
| allfields |
10.1007/s10845-022-01956-7 doi (DE-627)OLC2143778708 (DE-He213)s10845-022-01956-7-p DE-627 ger DE-627 rakwb eng 620 004 VZ Huang, Sihan verfasserin aut Part family formation method for delayed reconfigurable manufacturing system based on machine learning 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of part family formation. To bring the characteristics of delayed reconfiguration into the part family of D-RMS, an exclusive part family formation method for D-RMS based on machine learning is proposed in this paper. Firstly, a similarity coefficient that considers the characteristics of D-RMS is put forward based on the operation sequence of part. The positions of the common operations in the corresponding operation sequences are investigated. The more former common operations there are, the more probability it is that the parts are grouped into the same part family. The relative positions of the common operations are considered by proposing a concept of the longest relative position common operation subsequence (LPCS). Additionally, the position difference and discontinuity of the LPCSs in the corresponding operation sequences are analyzed. A similarity coefficient is proposed that incorporates the abovementioned factors. Secondly, a machine learning method named K-medoids is adopted to group parts into families based on the calculation result of the similarity coefficient. Finally, a case study is presented to implement the proposed part family formation method for D-RMS, where the effectiveness of the proposed method is verified through comparison. Delayed reconfigurable manufacturing system Part family formation Similarity coefficient Machine learning K-medoids Wang, Guoxin (orcid)0000-0003-2363-8595 aut Nie, Shiqi aut Wang, Bin aut Yan, Yan aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 34(2022), 6 vom: 14. Juni, Seite 2849-2863 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:34 year:2022 number:6 day:14 month:06 pages:2849-2863 https://doi.org/10.1007/s10845-022-01956-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 34 2022 6 14 06 2849-2863 |
| spelling |
10.1007/s10845-022-01956-7 doi (DE-627)OLC2143778708 (DE-He213)s10845-022-01956-7-p DE-627 ger DE-627 rakwb eng 620 004 VZ Huang, Sihan verfasserin aut Part family formation method for delayed reconfigurable manufacturing system based on machine learning 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of part family formation. To bring the characteristics of delayed reconfiguration into the part family of D-RMS, an exclusive part family formation method for D-RMS based on machine learning is proposed in this paper. Firstly, a similarity coefficient that considers the characteristics of D-RMS is put forward based on the operation sequence of part. The positions of the common operations in the corresponding operation sequences are investigated. The more former common operations there are, the more probability it is that the parts are grouped into the same part family. The relative positions of the common operations are considered by proposing a concept of the longest relative position common operation subsequence (LPCS). Additionally, the position difference and discontinuity of the LPCSs in the corresponding operation sequences are analyzed. A similarity coefficient is proposed that incorporates the abovementioned factors. Secondly, a machine learning method named K-medoids is adopted to group parts into families based on the calculation result of the similarity coefficient. Finally, a case study is presented to implement the proposed part family formation method for D-RMS, where the effectiveness of the proposed method is verified through comparison. Delayed reconfigurable manufacturing system Part family formation Similarity coefficient Machine learning K-medoids Wang, Guoxin (orcid)0000-0003-2363-8595 aut Nie, Shiqi aut Wang, Bin aut Yan, Yan aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 34(2022), 6 vom: 14. Juni, Seite 2849-2863 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:34 year:2022 number:6 day:14 month:06 pages:2849-2863 https://doi.org/10.1007/s10845-022-01956-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 34 2022 6 14 06 2849-2863 |
| allfields_unstemmed |
10.1007/s10845-022-01956-7 doi (DE-627)OLC2143778708 (DE-He213)s10845-022-01956-7-p DE-627 ger DE-627 rakwb eng 620 004 VZ Huang, Sihan verfasserin aut Part family formation method for delayed reconfigurable manufacturing system based on machine learning 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of part family formation. To bring the characteristics of delayed reconfiguration into the part family of D-RMS, an exclusive part family formation method for D-RMS based on machine learning is proposed in this paper. Firstly, a similarity coefficient that considers the characteristics of D-RMS is put forward based on the operation sequence of part. The positions of the common operations in the corresponding operation sequences are investigated. The more former common operations there are, the more probability it is that the parts are grouped into the same part family. The relative positions of the common operations are considered by proposing a concept of the longest relative position common operation subsequence (LPCS). Additionally, the position difference and discontinuity of the LPCSs in the corresponding operation sequences are analyzed. A similarity coefficient is proposed that incorporates the abovementioned factors. Secondly, a machine learning method named K-medoids is adopted to group parts into families based on the calculation result of the similarity coefficient. Finally, a case study is presented to implement the proposed part family formation method for D-RMS, where the effectiveness of the proposed method is verified through comparison. Delayed reconfigurable manufacturing system Part family formation Similarity coefficient Machine learning K-medoids Wang, Guoxin (orcid)0000-0003-2363-8595 aut Nie, Shiqi aut Wang, Bin aut Yan, Yan aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 34(2022), 6 vom: 14. Juni, Seite 2849-2863 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:34 year:2022 number:6 day:14 month:06 pages:2849-2863 https://doi.org/10.1007/s10845-022-01956-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 34 2022 6 14 06 2849-2863 |
| allfieldsGer |
10.1007/s10845-022-01956-7 doi (DE-627)OLC2143778708 (DE-He213)s10845-022-01956-7-p DE-627 ger DE-627 rakwb eng 620 004 VZ Huang, Sihan verfasserin aut Part family formation method for delayed reconfigurable manufacturing system based on machine learning 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of part family formation. To bring the characteristics of delayed reconfiguration into the part family of D-RMS, an exclusive part family formation method for D-RMS based on machine learning is proposed in this paper. Firstly, a similarity coefficient that considers the characteristics of D-RMS is put forward based on the operation sequence of part. The positions of the common operations in the corresponding operation sequences are investigated. The more former common operations there are, the more probability it is that the parts are grouped into the same part family. The relative positions of the common operations are considered by proposing a concept of the longest relative position common operation subsequence (LPCS). Additionally, the position difference and discontinuity of the LPCSs in the corresponding operation sequences are analyzed. A similarity coefficient is proposed that incorporates the abovementioned factors. Secondly, a machine learning method named K-medoids is adopted to group parts into families based on the calculation result of the similarity coefficient. Finally, a case study is presented to implement the proposed part family formation method for D-RMS, where the effectiveness of the proposed method is verified through comparison. Delayed reconfigurable manufacturing system Part family formation Similarity coefficient Machine learning K-medoids Wang, Guoxin (orcid)0000-0003-2363-8595 aut Nie, Shiqi aut Wang, Bin aut Yan, Yan aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 34(2022), 6 vom: 14. Juni, Seite 2849-2863 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:34 year:2022 number:6 day:14 month:06 pages:2849-2863 https://doi.org/10.1007/s10845-022-01956-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 34 2022 6 14 06 2849-2863 |
| allfieldsSound |
10.1007/s10845-022-01956-7 doi (DE-627)OLC2143778708 (DE-He213)s10845-022-01956-7-p DE-627 ger DE-627 rakwb eng 620 004 VZ Huang, Sihan verfasserin aut Part family formation method for delayed reconfigurable manufacturing system based on machine learning 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of part family formation. To bring the characteristics of delayed reconfiguration into the part family of D-RMS, an exclusive part family formation method for D-RMS based on machine learning is proposed in this paper. Firstly, a similarity coefficient that considers the characteristics of D-RMS is put forward based on the operation sequence of part. The positions of the common operations in the corresponding operation sequences are investigated. The more former common operations there are, the more probability it is that the parts are grouped into the same part family. The relative positions of the common operations are considered by proposing a concept of the longest relative position common operation subsequence (LPCS). Additionally, the position difference and discontinuity of the LPCSs in the corresponding operation sequences are analyzed. A similarity coefficient is proposed that incorporates the abovementioned factors. Secondly, a machine learning method named K-medoids is adopted to group parts into families based on the calculation result of the similarity coefficient. Finally, a case study is presented to implement the proposed part family formation method for D-RMS, where the effectiveness of the proposed method is verified through comparison. Delayed reconfigurable manufacturing system Part family formation Similarity coefficient Machine learning K-medoids Wang, Guoxin (orcid)0000-0003-2363-8595 aut Nie, Shiqi aut Wang, Bin aut Yan, Yan aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 34(2022), 6 vom: 14. Juni, Seite 2849-2863 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:34 year:2022 number:6 day:14 month:06 pages:2849-2863 https://doi.org/10.1007/s10845-022-01956-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 34 2022 6 14 06 2849-2863 |
| language |
English |
| source |
Enthalten in Journal of intelligent manufacturing 34(2022), 6 vom: 14. Juni, Seite 2849-2863 volume:34 year:2022 number:6 day:14 month:06 pages:2849-2863 |
| sourceStr |
Enthalten in Journal of intelligent manufacturing 34(2022), 6 vom: 14. Juni, Seite 2849-2863 volume:34 year:2022 number:6 day:14 month:06 pages:2849-2863 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Delayed reconfigurable manufacturing system Part family formation Similarity coefficient Machine learning K-medoids |
| dewey-raw |
620 |
| isfreeaccess_bool |
false |
| container_title |
Journal of intelligent manufacturing |
| authorswithroles_txt_mv |
Huang, Sihan @@aut@@ Wang, Guoxin @@aut@@ Nie, Shiqi @@aut@@ Wang, Bin @@aut@@ Yan, Yan @@aut@@ |
| publishDateDaySort_date |
2022-06-14T00:00:00Z |
| hierarchy_top_id |
130892815 |
| dewey-sort |
3620 |
| id |
OLC2143778708 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2143778708</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118091713.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10845-022-01956-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2143778708</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10845-022-01956-7-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">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Huang, Sihan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Part family formation method for delayed reconfigurable manufacturing system based on machine learning</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of part family formation. To bring the characteristics of delayed reconfiguration into the part family of D-RMS, an exclusive part family formation method for D-RMS based on machine learning is proposed in this paper. Firstly, a similarity coefficient that considers the characteristics of D-RMS is put forward based on the operation sequence of part. The positions of the common operations in the corresponding operation sequences are investigated. The more former common operations there are, the more probability it is that the parts are grouped into the same part family. The relative positions of the common operations are considered by proposing a concept of the longest relative position common operation subsequence (LPCS). Additionally, the position difference and discontinuity of the LPCSs in the corresponding operation sequences are analyzed. A similarity coefficient is proposed that incorporates the abovementioned factors. Secondly, a machine learning method named K-medoids is adopted to group parts into families based on the calculation result of the similarity coefficient. Finally, a case study is presented to implement the proposed part family formation method for D-RMS, where the effectiveness of the proposed method is verified through comparison.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Delayed reconfigurable manufacturing system</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Part family formation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Similarity coefficient</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">K-medoids</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Guoxin</subfield><subfield code="0">(orcid)0000-0003-2363-8595</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nie, Shiqi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Bin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yan, Yan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of intelligent manufacturing</subfield><subfield code="d">Springer US, 1990</subfield><subfield code="g">34(2022), 6 vom: 14. Juni, Seite 2849-2863</subfield><subfield code="w">(DE-627)130892815</subfield><subfield code="w">(DE-600)1041378-9</subfield><subfield code="w">(DE-576)026321106</subfield><subfield code="x">0956-5515</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:34</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:6</subfield><subfield code="g">day:14</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:2849-2863</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10845-022-01956-7</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">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">34</subfield><subfield code="j">2022</subfield><subfield code="e">6</subfield><subfield code="b">14</subfield><subfield code="c">06</subfield><subfield code="h">2849-2863</subfield></datafield></record></collection>
|
| author |
Huang, Sihan |
| spellingShingle |
Huang, Sihan ddc 620 misc Delayed reconfigurable manufacturing system misc Part family formation misc Similarity coefficient misc Machine learning misc K-medoids Part family formation method for delayed reconfigurable manufacturing system based on machine learning |
| authorStr |
Huang, Sihan |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)130892815 |
| format |
Article |
| dewey-ones |
620 - Engineering & allied operations 004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0956-5515 |
| topic_title |
620 004 VZ Part family formation method for delayed reconfigurable manufacturing system based on machine learning Delayed reconfigurable manufacturing system Part family formation Similarity coefficient Machine learning K-medoids |
| topic |
ddc 620 misc Delayed reconfigurable manufacturing system misc Part family formation misc Similarity coefficient misc Machine learning misc K-medoids |
| topic_unstemmed |
ddc 620 misc Delayed reconfigurable manufacturing system misc Part family formation misc Similarity coefficient misc Machine learning misc K-medoids |
| topic_browse |
ddc 620 misc Delayed reconfigurable manufacturing system misc Part family formation misc Similarity coefficient misc Machine learning misc K-medoids |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Journal of intelligent manufacturing |
| hierarchy_parent_id |
130892815 |
| dewey-tens |
620 - Engineering 000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Journal of intelligent manufacturing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 |
| title |
Part family formation method for delayed reconfigurable manufacturing system based on machine learning |
| ctrlnum |
(DE-627)OLC2143778708 (DE-He213)s10845-022-01956-7-p |
| title_full |
Part family formation method for delayed reconfigurable manufacturing system based on machine learning |
| author_sort |
Huang, Sihan |
| journal |
Journal of intelligent manufacturing |
| journalStr |
Journal of intelligent manufacturing |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology 000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
txt |
| container_start_page |
2849 |
| author_browse |
Huang, Sihan Wang, Guoxin Nie, Shiqi Wang, Bin Yan, Yan |
| container_volume |
34 |
| class |
620 004 VZ |
| format_se |
Aufsätze |
| author-letter |
Huang, Sihan |
| doi_str_mv |
10.1007/s10845-022-01956-7 |
| normlink |
(ORCID)0000-0003-2363-8595 |
| normlink_prefix_str_mv |
(orcid)0000-0003-2363-8595 |
| dewey-full |
620 004 |
| title_sort |
part family formation method for delayed reconfigurable manufacturing system based on machine learning |
| title_auth |
Part family formation method for delayed reconfigurable manufacturing system based on machine learning |
| abstract |
Abstract Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of part family formation. To bring the characteristics of delayed reconfiguration into the part family of D-RMS, an exclusive part family formation method for D-RMS based on machine learning is proposed in this paper. Firstly, a similarity coefficient that considers the characteristics of D-RMS is put forward based on the operation sequence of part. The positions of the common operations in the corresponding operation sequences are investigated. The more former common operations there are, the more probability it is that the parts are grouped into the same part family. The relative positions of the common operations are considered by proposing a concept of the longest relative position common operation subsequence (LPCS). Additionally, the position difference and discontinuity of the LPCSs in the corresponding operation sequences are analyzed. A similarity coefficient is proposed that incorporates the abovementioned factors. Secondly, a machine learning method named K-medoids is adopted to group parts into families based on the calculation result of the similarity coefficient. Finally, a case study is presented to implement the proposed part family formation method for D-RMS, where the effectiveness of the proposed method is verified through comparison. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of part family formation. To bring the characteristics of delayed reconfiguration into the part family of D-RMS, an exclusive part family formation method for D-RMS based on machine learning is proposed in this paper. Firstly, a similarity coefficient that considers the characteristics of D-RMS is put forward based on the operation sequence of part. The positions of the common operations in the corresponding operation sequences are investigated. The more former common operations there are, the more probability it is that the parts are grouped into the same part family. The relative positions of the common operations are considered by proposing a concept of the longest relative position common operation subsequence (LPCS). Additionally, the position difference and discontinuity of the LPCSs in the corresponding operation sequences are analyzed. A similarity coefficient is proposed that incorporates the abovementioned factors. Secondly, a machine learning method named K-medoids is adopted to group parts into families based on the calculation result of the similarity coefficient. Finally, a case study is presented to implement the proposed part family formation method for D-RMS, where the effectiveness of the proposed method is verified through comparison. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of part family formation. To bring the characteristics of delayed reconfiguration into the part family of D-RMS, an exclusive part family formation method for D-RMS based on machine learning is proposed in this paper. Firstly, a similarity coefficient that considers the characteristics of D-RMS is put forward based on the operation sequence of part. The positions of the common operations in the corresponding operation sequences are investigated. The more former common operations there are, the more probability it is that the parts are grouped into the same part family. The relative positions of the common operations are considered by proposing a concept of the longest relative position common operation subsequence (LPCS). Additionally, the position difference and discontinuity of the LPCSs in the corresponding operation sequences are analyzed. A similarity coefficient is proposed that incorporates the abovementioned factors. Secondly, a machine learning method named K-medoids is adopted to group parts into families based on the calculation result of the similarity coefficient. Finally, a case study is presented to implement the proposed part family formation method for D-RMS, where the effectiveness of the proposed method is verified through comparison. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT |
| container_issue |
6 |
| title_short |
Part family formation method for delayed reconfigurable manufacturing system based on machine learning |
| url |
https://doi.org/10.1007/s10845-022-01956-7 |
| remote_bool |
false |
| author2 |
Wang, Guoxin Nie, Shiqi Wang, Bin Yan, Yan |
| author2Str |
Wang, Guoxin Nie, Shiqi Wang, Bin Yan, Yan |
| ppnlink |
130892815 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10845-022-01956-7 |
| up_date |
2024-07-03T18:01:41.663Z |
| _version_ |
1803581864453079040 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2143778708</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118091713.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10845-022-01956-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2143778708</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10845-022-01956-7-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">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Huang, Sihan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Part family formation method for delayed reconfigurable manufacturing system based on machine learning</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of part family formation. To bring the characteristics of delayed reconfiguration into the part family of D-RMS, an exclusive part family formation method for D-RMS based on machine learning is proposed in this paper. Firstly, a similarity coefficient that considers the characteristics of D-RMS is put forward based on the operation sequence of part. The positions of the common operations in the corresponding operation sequences are investigated. The more former common operations there are, the more probability it is that the parts are grouped into the same part family. The relative positions of the common operations are considered by proposing a concept of the longest relative position common operation subsequence (LPCS). Additionally, the position difference and discontinuity of the LPCSs in the corresponding operation sequences are analyzed. A similarity coefficient is proposed that incorporates the abovementioned factors. Secondly, a machine learning method named K-medoids is adopted to group parts into families based on the calculation result of the similarity coefficient. Finally, a case study is presented to implement the proposed part family formation method for D-RMS, where the effectiveness of the proposed method is verified through comparison.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Delayed reconfigurable manufacturing system</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Part family formation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Similarity coefficient</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">K-medoids</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Guoxin</subfield><subfield code="0">(orcid)0000-0003-2363-8595</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nie, Shiqi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Bin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yan, Yan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of intelligent manufacturing</subfield><subfield code="d">Springer US, 1990</subfield><subfield code="g">34(2022), 6 vom: 14. Juni, Seite 2849-2863</subfield><subfield code="w">(DE-627)130892815</subfield><subfield code="w">(DE-600)1041378-9</subfield><subfield code="w">(DE-576)026321106</subfield><subfield code="x">0956-5515</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:34</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:6</subfield><subfield code="g">day:14</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:2849-2863</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10845-022-01956-7</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">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">34</subfield><subfield code="j">2022</subfield><subfield code="e">6</subfield><subfield code="b">14</subfield><subfield code="c">06</subfield><subfield code="h">2849-2863</subfield></datafield></record></collection>
|
| score |
7.401865 |