Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint
This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into acc...
Ausführliche Beschreibung
Autor*in: |
Liu, Jikai [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2017transfer abstract |
---|
Schlagwörter: |
---|
Umfang: |
19 |
---|
Übergeordnetes Werk: |
Enthalten in: Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study) - Tack, Jan F. ELSEVIER, 2014, CAD, Amsterdam [u.a.] |
---|---|
Übergeordnetes Werk: |
volume:91 ; year:2017 ; pages:27-45 ; extent:19 |
Links: |
---|
DOI / URN: |
10.1016/j.cad.2017.05.003 |
---|
Katalog-ID: |
ELV036142115 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | ELV036142115 | ||
003 | DE-627 | ||
005 | 20230625210952.0 | ||
007 | cr uuu---uuuuu | ||
008 | 180603s2017 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.cad.2017.05.003 |2 doi | |
028 | 5 | 2 | |a GBVA2017020000009.pica |
035 | |a (DE-627)ELV036142115 | ||
035 | |a (ELSEVIER)S0010-4485(17)30063-5 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | |a 004 |a 600 | |
082 | 0 | 4 | |a 004 |q DE-600 |
082 | 0 | 4 | |a 600 |q DE-600 |
082 | 0 | 4 | |a 610 |q VZ |
082 | 0 | 4 | |a 570 |q VZ |
084 | |a BIODIV |q DE-30 |2 fid | ||
084 | |a 35.70 |2 bkl | ||
084 | |a 42.12 |2 bkl | ||
084 | |a 42.15 |2 bkl | ||
100 | 1 | |a Liu, Jikai |e verfasserin |4 aut | |
245 | 1 | 0 | |a Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint |
264 | 1 | |c 2017transfer abstract | |
300 | |a 19 | ||
336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
337 | |a nicht spezifiziert |b z |2 rdamedia | ||
338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
520 | |a This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. | ||
520 | |a This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. | ||
650 | 7 | |a Manufacturability constraint |2 Elsevier | |
650 | 7 | |a Topology optimization |2 Elsevier | |
650 | 7 | |a Additive manufacturing |2 Elsevier | |
650 | 7 | |a Deposition path planning |2 Elsevier | |
700 | 1 | |a To, Albert C. |4 oth | |
773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Tack, Jan F. ELSEVIER |t Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study) |d 2014 |d CAD |g Amsterdam [u.a.] |w (DE-627)ELV012614920 |
773 | 1 | 8 | |g volume:91 |g year:2017 |g pages:27-45 |g extent:19 |
856 | 4 | 0 | |u https://doi.org/10.1016/j.cad.2017.05.003 |3 Volltext |
912 | |a GBV_USEFLAG_U | ||
912 | |a GBV_ELV | ||
912 | |a SYSFLAG_U | ||
912 | |a FID-BIODIV | ||
912 | |a SSG-OLC-PHA | ||
912 | |a GBV_ILN_22 | ||
912 | |a GBV_ILN_40 | ||
912 | |a GBV_ILN_105 | ||
936 | b | k | |a 35.70 |j Biochemie: Allgemeines |q VZ |
936 | b | k | |a 42.12 |j Biophysik |q VZ |
936 | b | k | |a 42.15 |j Zellbiologie |q VZ |
951 | |a AR | ||
952 | |d 91 |j 2017 |h 27-45 |g 19 | ||
953 | |2 045F |a 004 |
author_variant |
j l jl |
---|---|
matchkey_str |
liujikaitoalbertc:2017----:eoiinahlnignertdtutrloooypiiaino3adtvmnfcui |
hierarchy_sort_str |
2017transfer abstract |
bklnumber |
35.70 42.12 42.15 |
publishDate |
2017 |
allfields |
10.1016/j.cad.2017.05.003 doi GBVA2017020000009.pica (DE-627)ELV036142115 (ELSEVIER)S0010-4485(17)30063-5 DE-627 ger DE-627 rakwb eng 004 600 004 DE-600 600 DE-600 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Liu, Jikai verfasserin aut Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint 2017transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. Manufacturability constraint Elsevier Topology optimization Elsevier Additive manufacturing Elsevier Deposition path planning Elsevier To, Albert C. oth Enthalten in Elsevier Science Tack, Jan F. ELSEVIER Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study) 2014 CAD Amsterdam [u.a.] (DE-627)ELV012614920 volume:91 year:2017 pages:27-45 extent:19 https://doi.org/10.1016/j.cad.2017.05.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 91 2017 27-45 19 045F 004 |
spelling |
10.1016/j.cad.2017.05.003 doi GBVA2017020000009.pica (DE-627)ELV036142115 (ELSEVIER)S0010-4485(17)30063-5 DE-627 ger DE-627 rakwb eng 004 600 004 DE-600 600 DE-600 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Liu, Jikai verfasserin aut Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint 2017transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. Manufacturability constraint Elsevier Topology optimization Elsevier Additive manufacturing Elsevier Deposition path planning Elsevier To, Albert C. oth Enthalten in Elsevier Science Tack, Jan F. ELSEVIER Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study) 2014 CAD Amsterdam [u.a.] (DE-627)ELV012614920 volume:91 year:2017 pages:27-45 extent:19 https://doi.org/10.1016/j.cad.2017.05.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 91 2017 27-45 19 045F 004 |
allfields_unstemmed |
10.1016/j.cad.2017.05.003 doi GBVA2017020000009.pica (DE-627)ELV036142115 (ELSEVIER)S0010-4485(17)30063-5 DE-627 ger DE-627 rakwb eng 004 600 004 DE-600 600 DE-600 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Liu, Jikai verfasserin aut Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint 2017transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. Manufacturability constraint Elsevier Topology optimization Elsevier Additive manufacturing Elsevier Deposition path planning Elsevier To, Albert C. oth Enthalten in Elsevier Science Tack, Jan F. ELSEVIER Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study) 2014 CAD Amsterdam [u.a.] (DE-627)ELV012614920 volume:91 year:2017 pages:27-45 extent:19 https://doi.org/10.1016/j.cad.2017.05.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 91 2017 27-45 19 045F 004 |
allfieldsGer |
10.1016/j.cad.2017.05.003 doi GBVA2017020000009.pica (DE-627)ELV036142115 (ELSEVIER)S0010-4485(17)30063-5 DE-627 ger DE-627 rakwb eng 004 600 004 DE-600 600 DE-600 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Liu, Jikai verfasserin aut Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint 2017transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. Manufacturability constraint Elsevier Topology optimization Elsevier Additive manufacturing Elsevier Deposition path planning Elsevier To, Albert C. oth Enthalten in Elsevier Science Tack, Jan F. ELSEVIER Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study) 2014 CAD Amsterdam [u.a.] (DE-627)ELV012614920 volume:91 year:2017 pages:27-45 extent:19 https://doi.org/10.1016/j.cad.2017.05.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 91 2017 27-45 19 045F 004 |
allfieldsSound |
10.1016/j.cad.2017.05.003 doi GBVA2017020000009.pica (DE-627)ELV036142115 (ELSEVIER)S0010-4485(17)30063-5 DE-627 ger DE-627 rakwb eng 004 600 004 DE-600 600 DE-600 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Liu, Jikai verfasserin aut Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint 2017transfer abstract 19 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. Manufacturability constraint Elsevier Topology optimization Elsevier Additive manufacturing Elsevier Deposition path planning Elsevier To, Albert C. oth Enthalten in Elsevier Science Tack, Jan F. ELSEVIER Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study) 2014 CAD Amsterdam [u.a.] (DE-627)ELV012614920 volume:91 year:2017 pages:27-45 extent:19 https://doi.org/10.1016/j.cad.2017.05.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 91 2017 27-45 19 045F 004 |
language |
English |
source |
Enthalten in Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study) Amsterdam [u.a.] volume:91 year:2017 pages:27-45 extent:19 |
sourceStr |
Enthalten in Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study) Amsterdam [u.a.] volume:91 year:2017 pages:27-45 extent:19 |
format_phy_str_mv |
Article |
bklname |
Biochemie: Allgemeines Biophysik Zellbiologie |
institution |
findex.gbv.de |
topic_facet |
Manufacturability constraint Topology optimization Additive manufacturing Deposition path planning |
dewey-raw |
004 |
isfreeaccess_bool |
false |
container_title |
Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study) |
authorswithroles_txt_mv |
Liu, Jikai @@aut@@ To, Albert C. @@oth@@ |
publishDateDaySort_date |
2017-01-01T00:00:00Z |
hierarchy_top_id |
ELV012614920 |
dewey-sort |
14 |
id |
ELV036142115 |
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">ELV036142115</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625210952.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.cad.2017.05.003</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2017020000009.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV036142115</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0010-4485(17)30063-5</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=" "><subfield code="a">004</subfield><subfield code="a">600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">600</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.15</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liu, Jikai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">19</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Manufacturability constraint</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Topology optimization</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Additive manufacturing</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Deposition path planning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">To, Albert C.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Tack, Jan F. ELSEVIER</subfield><subfield code="t">Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study)</subfield><subfield code="d">2014</subfield><subfield code="d">CAD</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV012614920</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:91</subfield><subfield code="g">year:2017</subfield><subfield code="g">pages:27-45</subfield><subfield code="g">extent:19</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.cad.2017.05.003</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.15</subfield><subfield code="j">Zellbiologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">91</subfield><subfield code="j">2017</subfield><subfield code="h">27-45</subfield><subfield code="g">19</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">004</subfield></datafield></record></collection>
|
author |
Liu, Jikai |
spellingShingle |
Liu, Jikai ddc 004 ddc 600 ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 bkl 42.15 Elsevier Manufacturability constraint Elsevier Topology optimization Elsevier Additive manufacturing Elsevier Deposition path planning Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint |
authorStr |
Liu, Jikai |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV012614920 |
format |
electronic Article |
dewey-ones |
004 - Data processing & computer science 600 - Technology 610 - Medicine & health 570 - Life sciences; biology |
delete_txt_mv |
keep |
author_role |
aut |
collection |
elsevier |
remote_str |
true |
illustrated |
Not Illustrated |
topic_title |
004 600 004 DE-600 600 DE-600 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint Manufacturability constraint Elsevier Topology optimization Elsevier Additive manufacturing Elsevier Deposition path planning Elsevier |
topic |
ddc 004 ddc 600 ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 bkl 42.15 Elsevier Manufacturability constraint Elsevier Topology optimization Elsevier Additive manufacturing Elsevier Deposition path planning |
topic_unstemmed |
ddc 004 ddc 600 ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 bkl 42.15 Elsevier Manufacturability constraint Elsevier Topology optimization Elsevier Additive manufacturing Elsevier Deposition path planning |
topic_browse |
ddc 004 ddc 600 ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 bkl 42.15 Elsevier Manufacturability constraint Elsevier Topology optimization Elsevier Additive manufacturing Elsevier Deposition path planning |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
zu |
author2_variant |
a c t ac act |
hierarchy_parent_title |
Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study) |
hierarchy_parent_id |
ELV012614920 |
dewey-tens |
000 - Computer science, knowledge & systems 600 - Technology 610 - Medicine & health 570 - Life sciences; biology |
hierarchy_top_title |
Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study) |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)ELV012614920 |
title |
Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint |
ctrlnum |
(DE-627)ELV036142115 (ELSEVIER)S0010-4485(17)30063-5 |
title_full |
Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint |
author_sort |
Liu, Jikai |
journal |
Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study) |
journalStr |
Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study) |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
000 - Computer science, information & general works 600 - Technology 500 - Science |
recordtype |
marc |
publishDateSort |
2017 |
contenttype_str_mv |
zzz |
container_start_page |
27 |
author_browse |
Liu, Jikai |
container_volume |
91 |
physical |
19 |
class |
004 600 004 DE-600 600 DE-600 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl |
format_se |
Elektronische Aufsätze |
author-letter |
Liu, Jikai |
doi_str_mv |
10.1016/j.cad.2017.05.003 |
dewey-full |
004 600 610 570 |
title_sort |
deposition path planning-integrated structural topology optimization for 3d additive manufacturing subject to self-support constraint |
title_auth |
Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint |
abstract |
This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. |
abstractGer |
This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. |
abstract_unstemmed |
This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed. |
collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 |
title_short |
Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint |
url |
https://doi.org/10.1016/j.cad.2017.05.003 |
remote_bool |
true |
author2 |
To, Albert C. |
author2Str |
To, Albert C. |
ppnlink |
ELV012614920 |
mediatype_str_mv |
z |
isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth |
doi_str |
10.1016/j.cad.2017.05.003 |
up_date |
2024-07-06T19:24:44.448Z |
_version_ |
1803858880179994624 |
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">ELV036142115</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625210952.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.cad.2017.05.003</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2017020000009.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV036142115</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0010-4485(17)30063-5</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=" "><subfield code="a">004</subfield><subfield code="a">600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">600</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.15</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liu, Jikai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">19</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Manufacturability constraint</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Topology optimization</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Additive manufacturing</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Deposition path planning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">To, Albert C.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Tack, Jan F. ELSEVIER</subfield><subfield code="t">Sa1080 Concordance Among Outcome Variables Assessing the Response to Treatment With Ibodutant in Irritable Bowel Syndrome With Diarrhea (IBS-D; the IRIS-2 Study)</subfield><subfield code="d">2014</subfield><subfield code="d">CAD</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV012614920</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:91</subfield><subfield code="g">year:2017</subfield><subfield code="g">pages:27-45</subfield><subfield code="g">extent:19</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.cad.2017.05.003</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.15</subfield><subfield code="j">Zellbiologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">91</subfield><subfield code="j">2017</subfield><subfield code="h">27-45</subfield><subfield code="g">19</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">004</subfield></datafield></record></collection>
|
score |
7.4001493 |