This paper proposes an end-to-end method for automatic assembly quality inspection based on a point cloud domain adaptation model. The method involves automatically generating labeled point clouds from various CAD models and training a model on those point clouds together with a limited number of unlabeled point clouds acquired by 3D cameras. The model can then classify newly captured point clouds from 3D cameras to execute assembly quality inspection with promising performance. The method has been evaluated in an industry case study of pedal car front-wheel assembly. By utilizing CAD data, the method is less time-consuming for implementation in production.