  Deformation detection is vital for enabling accurate assessment and
prediction of structural changes in materials, ensuring timely and effective
interventions to maintain safety and integrity. Automating deformation
detection through computer vision is crucial for efficient monitoring, but it
faces significant challenges in creating a comprehensive dataset of both
deformed and non-deformed objects, which can be difficult to obtain in many
scenarios. In this paper, we introduce a novel framework for generating
controlled synthetic data that simulates deformed objects. This approach allows
for the realistic modeling of object deformations under various conditions. Our
framework integrates an intelligent adapter network that facilitates
sim-to-real domain adaptation, enhancing classification results without
requiring real data from deformed objects. We conduct experiments on domain
adaptation and classification tasks and demonstrate that our framework improves
sim-to-real classification results compared to simulation baseline.
