This tutorial guides you along the steps to digitally analyze the statistical properties of a 3D scan of an engine mount and determine the material performance. First, we import the scanned images and analyze the 3D model, and then, we build a statistical representation of the material - a statistical Digital Twin. Afterwards, we assess the performance of the 3D structure modeled from the imported 3D scan and compare it to the performance of the statistical Digital Twin. In this way, we validate using the digital statistical twin for property prediction.

In detail, the tutorial teaches the user to do the following:

• Import the 3D scan of an engine mount with ImportGeo-Vol.
• Analyze the mechanical properties and fiber size distribution with ElastoDict and FiberFind - Identify Fibers (AI).
• Post-processing of the FiberFind - Identify Fibers (AI) results using GeoDexcel (requires Excel installation).
• Using the results of fiber identification, build a statistical twin with FiberGeo.
• Validate the usage for mechanical property prediction of the statistical twin with ElastoDict.

In the end, FiberFind - Identify Fibers (AI) provides quantitative insights into the 3D scan and the ElastoDict simulation results provide insights on the performance of the engine mount. In this way, you are a step closer to identifying the designs on which to concentrate for prototyping.