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...
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Veröffentlicht in: | NIPPON GOMU KYOKAISHI 2022, Vol.95(2), pp.40-46 |
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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 |
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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> |
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subjects | FEM Simulation Machine Learning Modulus Rubber Blend SHAP Analysis Structure-property Correlation Diagram |
title | Informatics Research in Functional Synthetic Rubber Materials |
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