New Fatlab release: 2.064

A new release with minor updates/bugfixes is available for download. Improvements include:

  • Reduced complexity: less .mex files due to performance improvements in standard Matlab.
  • Removed keyboard shortcuts for view manipulation (annoying when e.g. changing the colorbar scale values)
  • Cleanup in files that were longer used.
  • New feature: automatically exports the stress tensor vs. time for the selected node, when showing the “Stress” result under the Graph tab. Useful for custom calculations.
  • Fixed bug in the reported “Max stress range” under “Result summary”. It was calculated including zero (0).
  • Importing from Ansys now done without compiled .mex files, leading to easier debugging in case of problems importing Ansys models, e.g. with non-standard formatting.
  • Removed hotspot numbers, which were floating all over the place.

Potentially critical bug

It has just been discovered that the minimum cut-off of the SN curve (and maximum cut-off) was used without applying the partial safety factor. This is relevant in connection with SN curves which transition to “infinite life”, e.g. according to Eurocode 3 or some older textbooks on machine design. The bug leads to non-conservative results. It has been fixed in version 2.053.

Fatlab 2.041 released

New version is out with mostly bugfixes and a few improvements:

  • Handles long node numbers correctly (7+ digits).
  • Can save large models (+2GB).
  • Improved lightning: doesn’t rotate with the model.
  • Fixed bug in calculation of dominating load in a saved file solved using parallel computing toolbox.
  • Fixed bug in calculation of hotspot damage using the critical plane method.
  • Added new version of the Pnmax stress, called Pnma2. The new version is more robust in cases of a stress state close to pure shear, where P1 and P3 are almost equal.
  • Added option for specifying the Ansys stress listing output column width, if e.g. 7 digits is not enough for the node numbers.
  • Added detection of savefiles created with previous versions (which may be missing new data fields)
  • Going forward, backwards compatibility is attempted such that all new versions can load files from version 2.036 and onwards.
  • Some improvements in the anisotropy handling.

Fatlab 2.034 available. Now with anisotropy feature

A new version has just been released along with an associated example. This time, the main feature is the handling of anisotropic fatigue strength, as is needed in several cases, e.g. for additive manufacturing.

A relatively simple approach is taken, where the fatigue strength is scaled according to the surface angle of a given node relative to a user-defined reference direction, e.g. the direction of gravity during a print.

Video Tutorial

After many requests, I have now created a video tutorial showing how to get started using Fatlab. The video shows how to export the model and stress files from ANSYS and setup the analysis in Fatlab.

The video is on youtube. Use the high resolution to see what is going on. Hopefully more will follow and hopefully they will be better and without the annoying watermark.

Fatlab getting started tutorial.

Site updated

FatigueToolbox just got a major overhaul. In addition to the Fatlab project, it now contains several other fatigue resources such as publications and fatigue data. In the future, the site will be updated more regularly and with more broad fatigue topics.

Nice CT scans of 3D aluminum printed fatigue test specimens.

Minor updates

Fatlab and the examples have been updated to accommodate for changes in Matlab 2018b and ANSYS 18.2.

In Matlab, the figure window has been changed due to the new zoom features (using the mouse wheel).

In ANSYS, the default format of text based stress output has been changed to include one more zero in the exponent.

Parallel execution option available

As mentioned, the standalone version will be discontinued and only the source will be provided for running under Matlab. This is because the deployed version behaved slightly different than the source version, and ran much slower. So, going forward, development will not be halted by limitations of the deploytool.

One of the limiting issues of the deployed version was in parallel execution of the code. It ran fine in Matlab, but not when deployed. So now this feature has been re-implemented, as of Fatlab 2.022. It uses the Parallel Computing Toolbox. Fatlab will detect whether the toolbox is installed and enable it accordingly. The user can then select a number of cores for the execution under Run Analysis.

Special thanks to Martin Dahl Kilt for helping with this feature and performance issues.