Quality Checks
The specific volume models describe the behavior of two distinct domains of volumetric behavior, above and below a transition temperature (glass transition for amorphous polymers and melt for semicrystalline and others). The continuity between these two domains is generally established during the fitting process for the Tait and Schmidt-type models. The Renner model ensures continuity between solid and molten domains by design, eliminating the need for continuity checks. In Material Modeler, the error metrics include average error and standard deviation of the least squares error, and are used to evaluate the fit for both domains. Also, continuity is checked and reported separately when the pressure is zero and when the pressure is above zero. The "stop lights" provide an evaluation of the continuity versus what is allowed in the Altair Manufacturing Solver (AMS).
- Mean error over the data set in the Solid and Molten domains.
- Standard Deviation of the error in each domain.
- The maximum error in the continuity at the intersection of the domains at P = 0 and when P > 0. To ensure a smooth phase transition, it can be a problem if coefficients obtained from external sources or labs have significant discontinuities. This tool is useful for checking and fixing specific volume coefficients that may not pass quality checks for a packing analysis using Inspire Mold.
The quality checks show how well the connection works at the transition temperature. They also include measurements for the fit and the standard deviation for the zero-pressure curve (blue line - third row) and the pressure-dependent curves (fourth row). The plot has a black line showing the phase transition. It represents the transition temperature with respect to pressure.
The stop light indicators are associated only with continuity checks, while standard deviation describes fit precision. The specific volume at transition temperatures is calculated using both solid and molten coefficients, and the difference between these calculations is compared to ensure continuity.
Models like Tait are expected to show errors at pressures above zero due to the inherent imperfections in real data sets, though issues at zero pressure are typically minimal if parameters are altered.