PHYSICAL PROPERTY DATA PREDICTION METHOD AND PHYSICAL PROPERTY DATA PREDICTION DEVICE, AND FACTOR DATA PREDICTION METHOD AND FACTOR DATA PREDICTION DEVICE

To precisely perform prediction when allowing a computer to predict values of physical property data of a vulcanized rubber composition produced by vulcanizing an unvulcanized rubber composition obtained by mixing multiple raw materials.SOLUTION: A physical property data prediction method includes t...

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Bibliographic Details
Main Authors SUZUKI KIYOTO, TAKAHASHI TAKUJI
Format Patent
LanguageEnglish
Japanese
Published 18.05.2023
Subjects
CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES
MEASURING
PHYSICS
TESTING
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Summary:To precisely perform prediction when allowing a computer to predict values of physical property data of a vulcanized rubber composition produced by vulcanizing an unvulcanized rubber composition obtained by mixing multiple raw materials.SOLUTION: A physical property data prediction method includes the steps of: allowing a prediction model within the computer to perform machine learning by using learning data with values of physical property data regarding multiple vulcanized rubber compositions as learning output data and with information on a blend ratio of each of raw materials and information on processing conditions as learning input data; and allowing a prediction model to predict values of physical property data of a predicted vulcanized rubber composition by using information on a blend ratio of raw materials for constituting a predicted vulcanized rubber composition and information on processing conditions of a predicted vulcanized rubber composition. Information on processing conditions used for machine learning includes information on a heat history that a raw material receives in processing of the raw material for producing a vulcanized rubber composition.SELECTED DRAWING: Figure 1 【課題】複数の原材料を混合して得られる未加硫ゴム組成物を加硫することにより作製される加硫ゴム組成物の物性データの値を、コンピュータに予測させる際に、精度よく予測する。【解決手段】実施形態の物性データ予測方法は、複数の加硫ゴム組成物に関する物性データの値を学習用出力データとし、原材料それぞれの配合比率の情報と、加工条件の情報と、を学習用入力データとした学習データを用いてコンピュータ内の予測モデルに機械学習をさせるステップと、予測対象加硫ゴム組成物の構成原材料の配合比率の情報と、予測対象加硫ゴム組成物の加工条件の情報と、を用いて、予測対象加硫ゴム組成物の物性データの値を予測モデルに予測させるステップと、を備える。機械学習に用いられる加工条件の情報は、加硫ゴム組成物を作製するための原材料の加工により原材料が受けた熱履歴の情報を含む。【選択図】図１
Bibliography:Application Number: JP20210180353