  This paper addresses the challenge of geometric quality assurance in
manufacturing, particularly when human assessment is required. It proposes
using Blender, an open-source simulation tool, to create synthetic datasets for
machine learning (ML) models. The process involves translating expert
information into shape key parameters to simulate deformations, generating
images for both deformed and non-deformed objects. The study explores the
impact of discrepancies between real and simulated environments on ML model
performance and investigates the effect of different simulation backgrounds on
model sensitivity. Additionally, the study aims to enhance the model's
robustness to camera positioning by generating datasets with a variety of
randomized viewpoints. The entire process, from data synthesis to model
training and testing, is implemented using a Python API interfacing with
Blender. An experiment with a soda can object validates the accuracy of the
proposed pipeline.
