Visual Deformation Detection Using Soft Material Simulation for Pre-training of Condition Assessment Models
This addresses quality control in manufacturing where human assessment is needed, but it is incremental as it applies existing simulation tools to a specific domain.
The paper tackles geometric quality assurance in manufacturing by using Blender simulation to create synthetic datasets for machine learning models, with an experiment on a soda can object validating the pipeline's accuracy.
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.