Informatics Research in Functional Synthetic Rubber Materials

The relationship between structures and physical properties for rubber blend was investigated by materials informatics approach. SBR/IR/Silica blend rubber, which is commonly used as tire rubber, was targeted in this paper. Model structures were created on a computer, and physical properties were ca...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:NIPPON GOMU KYOKAISHI 2022, Vol.95(2), pp.40-46
Hauptverfasser: ADACHI, Takumi, ITOMI, Ken, YAMAMOTO, Ryouta, KUBOUCHI, Shou, MORITA, Jun, HORIUCHI, Shin, MORITA, Hiroshi
Format: Artikel
Sprache:eng ; jpn
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 46
container_issue 2
container_start_page 40
container_title NIPPON GOMU KYOKAISHI
container_volume 95
creator ADACHI, Takumi
ITOMI, Ken
YAMAMOTO, Ryouta
KUBOUCHI, Shou
MORITA, Jun
HORIUCHI, Shin
MORITA, Hiroshi
description The relationship between structures and physical properties for rubber blend was investigated by materials informatics approach. SBR/IR/Silica blend rubber, which is commonly used as tire rubber, was targeted in this paper. Model structures were created on a computer, and physical properties were calculated by finite element method (FEM) simulation. Datasets with structural features as explanatory variables and calculated values as objective variables were obtained by three-dimensional structure analysis for model structures. To clarify the contribution of each structural features to physical properties, machine learning was performed using the dataset with the modulus at 50% elongation (M50) as the objective variable. The prediction accuracy of the model was as high as 0.87 in R2 value. Furthermore, SHapley Additive exPlanation (SHAP) analysis revealed that the continuity of SBR phase and the shape of the phase separation interface are particularly important for physical characteristics. Finally, we created a structure-property correlation diagram that allow us to narrow down the compositions and structures that satisfied target physical properties without actual experiments. It follows that material development period will be shortened.
doi_str_mv 10.2324/gomu.95.40
format Article
fullrecord <record><control><sourceid>jstage_cross</sourceid><recordid>TN_cdi_crossref_primary_10_2324_gomu_95_40</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>article_gomu_95_2_95_40_article_char_en</sourcerecordid><originalsourceid>FETCH-LOGICAL-c89n-cdcf07e76d5d5037993c1e4bbcd9f927e874738c04ed4626013cd0b052d54b0b3</originalsourceid><addsrcrecordid>eNo9z0tLw0AUBeBBFAy1G39B1kLizczkMYsupFgtVITahbthHjdNpJnITLrovzclktWBy8fhHkIeM0gpo_z52HfnVOQphxsSZVXFE-Cc3pIIgIoEKP2-J8sQWg3AMsgpLyKy2rq6950aWhPiPQZU3jRx6-LN2Zmh7Z06xV8XNzQ4inh_1hp9_KEG9K06hQdyV4-By_9ckMPm9bB-T3afb9v1yy4xlXCJsaaGEsvC5jYHVgrBTIZca2NFLWiJVclLVhngaHlBC8iYsaDHD23ONWi2IE9TrfF9CB5r-evbTvmLzEBep8vrdClyyWHEqwn_hEEdcabKjwtOOFM6-fluGuUlOvYHPmVjtA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Informatics Research in Functional Synthetic Rubber Materials</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>ADACHI, Takumi ; ITOMI, Ken ; YAMAMOTO, Ryouta ; KUBOUCHI, Shou ; MORITA, Jun ; HORIUCHI, Shin ; MORITA, Hiroshi</creator><creatorcontrib>ADACHI, Takumi ; ITOMI, Ken ; YAMAMOTO, Ryouta ; KUBOUCHI, Shou ; MORITA, Jun ; HORIUCHI, Shin ; MORITA, Hiroshi</creatorcontrib><description>The relationship between structures and physical properties for rubber blend was investigated by materials informatics approach. SBR/IR/Silica blend rubber, which is commonly used as tire rubber, was targeted in this paper. Model structures were created on a computer, and physical properties were calculated by finite element method (FEM) simulation. Datasets with structural features as explanatory variables and calculated values as objective variables were obtained by three-dimensional structure analysis for model structures. To clarify the contribution of each structural features to physical properties, machine learning was performed using the dataset with the modulus at 50% elongation (M50) as the objective variable. The prediction accuracy of the model was as high as 0.87 in R2 value. Furthermore, SHapley Additive exPlanation (SHAP) analysis revealed that the continuity of SBR phase and the shape of the phase separation interface are particularly important for physical characteristics. Finally, we created a structure-property correlation diagram that allow us to narrow down the compositions and structures that satisfied target physical properties without actual experiments. It follows that material development period will be shortened.</description><identifier>ISSN: 0029-022X</identifier><identifier>EISSN: 1884-0442</identifier><identifier>DOI: 10.2324/gomu.95.40</identifier><language>eng ; jpn</language><publisher>THE SOCIRETY OF RUBBER SCIENCE AND TECHNOLOGYY, JAPAN</publisher><subject>FEM Simulation ; Machine Learning ; Modulus ; Rubber Blend ; SHAP Analysis ; Structure-property Correlation Diagram</subject><ispartof>NIPPON GOMU KYOKAISHI, 2022, Vol.95(2), pp.40-46</ispartof><rights>2022 The Society of Rubber Industry, Japan</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c89n-cdcf07e76d5d5037993c1e4bbcd9f927e874738c04ed4626013cd0b052d54b0b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,4009,27902,27903,27904</link.rule.ids></links><search><creatorcontrib>ADACHI, Takumi</creatorcontrib><creatorcontrib>ITOMI, Ken</creatorcontrib><creatorcontrib>YAMAMOTO, Ryouta</creatorcontrib><creatorcontrib>KUBOUCHI, Shou</creatorcontrib><creatorcontrib>MORITA, Jun</creatorcontrib><creatorcontrib>HORIUCHI, Shin</creatorcontrib><creatorcontrib>MORITA, Hiroshi</creatorcontrib><title>Informatics Research in Functional Synthetic Rubber Materials</title><title>NIPPON GOMU KYOKAISHI</title><addtitle>NIPPON GOMU KYOKAISHI</addtitle><description>The relationship between structures and physical properties for rubber blend was investigated by materials informatics approach. SBR/IR/Silica blend rubber, which is commonly used as tire rubber, was targeted in this paper. Model structures were created on a computer, and physical properties were calculated by finite element method (FEM) simulation. Datasets with structural features as explanatory variables and calculated values as objective variables were obtained by three-dimensional structure analysis for model structures. To clarify the contribution of each structural features to physical properties, machine learning was performed using the dataset with the modulus at 50% elongation (M50) as the objective variable. The prediction accuracy of the model was as high as 0.87 in R2 value. Furthermore, SHapley Additive exPlanation (SHAP) analysis revealed that the continuity of SBR phase and the shape of the phase separation interface are particularly important for physical characteristics. Finally, we created a structure-property correlation diagram that allow us to narrow down the compositions and structures that satisfied target physical properties without actual experiments. It follows that material development period will be shortened.</description><subject>FEM Simulation</subject><subject>Machine Learning</subject><subject>Modulus</subject><subject>Rubber Blend</subject><subject>SHAP Analysis</subject><subject>Structure-property Correlation Diagram</subject><issn>0029-022X</issn><issn>1884-0442</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNo9z0tLw0AUBeBBFAy1G39B1kLizczkMYsupFgtVITahbthHjdNpJnITLrovzclktWBy8fhHkIeM0gpo_z52HfnVOQphxsSZVXFE-Cc3pIIgIoEKP2-J8sQWg3AMsgpLyKy2rq6950aWhPiPQZU3jRx6-LN2Zmh7Z06xV8XNzQ4inh_1hp9_KEG9K06hQdyV4-By_9ckMPm9bB-T3afb9v1yy4xlXCJsaaGEsvC5jYHVgrBTIZca2NFLWiJVclLVhngaHlBC8iYsaDHD23ONWi2IE9TrfF9CB5r-evbTvmLzEBep8vrdClyyWHEqwn_hEEdcabKjwtOOFM6-fluGuUlOvYHPmVjtA</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>ADACHI, Takumi</creator><creator>ITOMI, Ken</creator><creator>YAMAMOTO, Ryouta</creator><creator>KUBOUCHI, Shou</creator><creator>MORITA, Jun</creator><creator>HORIUCHI, Shin</creator><creator>MORITA, Hiroshi</creator><general>THE SOCIRETY OF RUBBER SCIENCE AND TECHNOLOGYY, JAPAN</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>2022</creationdate><title>Informatics Research in Functional Synthetic Rubber Materials</title><author>ADACHI, Takumi ; ITOMI, Ken ; YAMAMOTO, Ryouta ; KUBOUCHI, Shou ; MORITA, Jun ; HORIUCHI, Shin ; MORITA, Hiroshi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c89n-cdcf07e76d5d5037993c1e4bbcd9f927e874738c04ed4626013cd0b052d54b0b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng ; jpn</language><creationdate>2022</creationdate><topic>FEM Simulation</topic><topic>Machine Learning</topic><topic>Modulus</topic><topic>Rubber Blend</topic><topic>SHAP Analysis</topic><topic>Structure-property Correlation Diagram</topic><toplevel>online_resources</toplevel><creatorcontrib>ADACHI, Takumi</creatorcontrib><creatorcontrib>ITOMI, Ken</creatorcontrib><creatorcontrib>YAMAMOTO, Ryouta</creatorcontrib><creatorcontrib>KUBOUCHI, Shou</creatorcontrib><creatorcontrib>MORITA, Jun</creatorcontrib><creatorcontrib>HORIUCHI, Shin</creatorcontrib><creatorcontrib>MORITA, Hiroshi</creatorcontrib><collection>CrossRef</collection><jtitle>NIPPON GOMU KYOKAISHI</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>ADACHI, Takumi</au><au>ITOMI, Ken</au><au>YAMAMOTO, Ryouta</au><au>KUBOUCHI, Shou</au><au>MORITA, Jun</au><au>HORIUCHI, Shin</au><au>MORITA, Hiroshi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Informatics Research in Functional Synthetic Rubber Materials</atitle><jtitle>NIPPON GOMU KYOKAISHI</jtitle><addtitle>NIPPON GOMU KYOKAISHI</addtitle><date>2022</date><risdate>2022</risdate><volume>95</volume><issue>2</issue><spage>40</spage><epage>46</epage><pages>40-46</pages><issn>0029-022X</issn><eissn>1884-0442</eissn><abstract>The relationship between structures and physical properties for rubber blend was investigated by materials informatics approach. SBR/IR/Silica blend rubber, which is commonly used as tire rubber, was targeted in this paper. Model structures were created on a computer, and physical properties were calculated by finite element method (FEM) simulation. Datasets with structural features as explanatory variables and calculated values as objective variables were obtained by three-dimensional structure analysis for model structures. To clarify the contribution of each structural features to physical properties, machine learning was performed using the dataset with the modulus at 50% elongation (M50) as the objective variable. The prediction accuracy of the model was as high as 0.87 in R2 value. Furthermore, SHapley Additive exPlanation (SHAP) analysis revealed that the continuity of SBR phase and the shape of the phase separation interface are particularly important for physical characteristics. Finally, we created a structure-property correlation diagram that allow us to narrow down the compositions and structures that satisfied target physical properties without actual experiments. It follows that material development period will be shortened.</abstract><pub>THE SOCIRETY OF RUBBER SCIENCE AND TECHNOLOGYY, JAPAN</pub><doi>10.2324/gomu.95.40</doi><tpages>7</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0029-022X
ispartof NIPPON GOMU KYOKAISHI, 2022, Vol.95(2), pp.40-46
issn 0029-022X
1884-0442
language eng ; jpn
recordid cdi_crossref_primary_10_2324_gomu_95_40
source Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects FEM Simulation
Machine Learning
Modulus
Rubber Blend
SHAP Analysis
Structure-property Correlation Diagram
title Informatics Research in Functional Synthetic Rubber Materials
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-26T21%3A50%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstage_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Informatics%20Research%20in%20Functional%20Synthetic%20Rubber%20Materials&rft.jtitle=NIPPON%20GOMU%20KYOKAISHI&rft.au=ADACHI,%20Takumi&rft.date=2022&rft.volume=95&rft.issue=2&rft.spage=40&rft.epage=46&rft.pages=40-46&rft.issn=0029-022X&rft.eissn=1884-0442&rft_id=info:doi/10.2324/gomu.95.40&rft_dat=%3Cjstage_cross%3Earticle_gomu_95_2_95_40_article_char_en%3C/jstage_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true