Squeak and Rattle Director Tutorials

SnRD can be used to evaluate and study squeak and rattle issues in various products and across many industries. Some of the typical usecase are-
  • Risk and Root Cause (Dynamic) Analysis- Evaluating squeak and rattle issues on a model for dynamic/transient loading condition.
  • Quality Feel (Static) Analysis- Evaluating squeak and rattle issues on a model for static/touch point locations.
  • Driving the vehicle exposed to thermal effects- Evaluating squeak and rattle issues on system, when imposed with multiple loading conditions,. I.e vehicle is parked under direct sunlight and is driven off.
  • Manufacturing Variations study- to forecast the squeak and rattle issues in the systems based on the manufacturing variations like, component thickness, attachment stiffness, modal damping, etc.

Files Required

Below is a list of files required to complete the tutorial activity for the various usecases covered here.

To reduce the footprint size, the following tutorial model files are no longer included in the local installation. Use the hyperlinks to download them directly. You can also find zipped tutorial model files and demo model files on Altair One via the Altair Community, Altair Marketplace, and Altair Connect sites. Altair recommends that you create an Altair One account and use it as your primary portal to access product documentation, a Knowledge Base, and customer support.

Table 1. Files Required
Folder File / Folder Description
001_model
  1. tutorial_ip_snr_model.hm
  2. geometriclines.stp
  1. Clean model without E-Lines
  2. CAD file containing geometric lines
002_dts_and_material_data
  1. dts_document.csv
  2. materialdb_snr.csv
  1. Dimension Tolerance Specifications CSV file
  2. User created material data.
003_loads
  1. excitation_X.csv
  2. excitation_Y.csv
  3. excitation_Z.csv
  4. excitation_XYZ.csv
Load definition files for X, Y, Z and combined loading direction.
004_model_with_elines
  1. tutorial_ip_snr_model.fem
  2. tutorial_ip_snr_model_pre_output.csv
  1. Model with pre-defined E-Lines
  2. Pre output file containing the E-Lines data.