Deformation values within additively manufactured parts vary across different machine and material combinations in real-world fabrication scenarios, especially when considering the numerous laser powder bed fusion (LPBF) machine manufacturers and powder material suppliers available. Before simulating your production part, you should calibrate your simulation software to help reduce variability. Calibration accounts for the difference between measured and simulated deformation.
The overall calibration process for Additive in Mechanical consists of obtaining measured distortion values from physical experiments and then performing a calibration simulation. The first part of the process, obtaining measured distortion from a calibration build, is exactly the same process as described in the Additive Print and Science Calibration Guide. Refer to the following topics from that document for details:
The objective of this calibration procedure is to determine a set of calibration coefficients to use when performing additive simulations in Ansys Mechanical. The coefficients to be determined depend on the simulation type and strain definition selected. This is summarized in the following table. SSF is Strain Scaling Factor and ASC is Anisotropic Scaling Coefficient. Throughout this document, we will distinguish between an SSF/TSSF-only calibration and an SSF combined with ASCs (or SSF + ASCs) calibration.
Calibration Type Simulation type / Strain definition Calibration Coefficients SSF only AM LPBF Inherent Strain / Isotropic SSF - - - AM LPBF Thermal-Structural Thermal SSF (TSSF) - - - SSF + ASCs AM LPBF Inherent Strain / Scan Pattern SSF Parallel ASC Perpendicular ASC Vertical ASC = 1 AM LPBF Inherent Strain / Thermal Strain SSF Parallel ASC Perpendicular ASC Vertical ASC = 1 The values of the calibration coefficient(s) compensate for the difference between an experimentally measured target deformation and a simulated deformation value obtained at the same location on a chosen calibration geometry. Using the calibrated coefficient(s) will greatly improve the simulation prediction accuracy of your production part when using the same simulation setting combinations, therefore increasing the chance of successful builds as well as reducing the cost of trial-and-error experiments.
For more information about calibration coefficients, see the beginning of this section.