DESIGN SUPPORT METHOD FOR METAL MATERIAL, PREDICTION MODEL GENERATION METHOD, METAL MATERIAL PRODUCTION METHOD, AND DESIGN SUPPORT APPARATUS

A design support method capable of accurately obtaining predicted values, while also considering production conditions of a metal material, and of reducing the time required for design is provided. The design support method uses a calculator to support design of metal material with desired character...

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Bibliographische Detailangaben
Hauptverfasser:	NAKATSUJI, Kazuhiro, YAMAGUCHI, Osamu, TAKAGI, Hiroyuki
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING CASTING CASTING OF METALS CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES CHANGING THE PHYSICAL STRUCTURE OF NON-FERROUS METALS ANDNON-FERROUS ALLOYS CHEMISTRY COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING FERROUS OR NON-FERROUS ALLOYS HANDLING RECORD CARRIERS METALLURGY PERFORMING OPERATIONS PHYSICS POWDER METALLURGY PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS TRANSPORTING TREATMENT OF ALLOYS OR NON-FERROUS METALS
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Beschreibung
Zusammenfassung:	A design support method capable of accurately obtaining predicted values, while also considering production conditions of a metal material, and of reducing the time required for design is provided. The design support method uses a calculator to support design of metal material with desired characteristics and includes searching for design conditions yielding the desired characteristics using a prediction model for predicting a characteristic value of the metal material from the design conditions, the prediction model being constructed based on past performance data associating the design conditions, including chemical composition and production conditions of the metal material, with the characteristic value. The design support method also includes presenting at least the chemical composition and production conditions among the design conditions that are searched for and correspond to the desired characteristics. The design conditions are searched for so that deviation among predicted values based on different training data sets is reduced.