The key application of the CALPHAD method is the prediction of phase formation and driving forces in real multicomponent alloys. This must be based on a systematic assessment of binary systems with consistent elemental (unary) data. The predictions in ternary alloys often require additional assessments of ternary phases, shared intermetallic crystal structures and interactions. Quaternary predictions based on that foundation very rarely require additional data, and none were required for five or more components. The PanMg thermodynamicThermodynamic database for Mg alloys has been continuously developed in this systematic way since 30 years. Recently the elemental data of Gd and Y and key binary subsystems, such as Mg–Zn, were revised. Consequently, many related binary and ternary systems also needed revision and the opportunity was seized to include all the latest experimental data. This current significant upgrade of the database leading to a “second generation” of PanMg is exemplified.

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Recent Significant Upgrade of the Thermodynamic Database PanMg and Application Examples

  • Rainer Schmid-Fetzer

摘要

The key application of the CALPHAD method is the prediction of phase formation and driving forces in real multicomponent alloys. This must be based on a systematic assessment of binary systems with consistent elemental (unary) data. The predictions in ternary alloys often require additional assessments of ternary phases, shared intermetallic crystal structures and interactions. Quaternary predictions based on that foundation very rarely require additional data, and none were required for five or more components. The PanMg thermodynamicThermodynamic database for Mg alloys has been continuously developed in this systematic way since 30 years. Recently the elemental data of Gd and Y and key binary subsystems, such as Mg–Zn, were revised. Consequently, many related binary and ternary systems also needed revision and the opportunity was seized to include all the latest experimental data. This current significant upgrade of the database leading to a “second generation” of PanMg is exemplified.