Deep learning retrieval of 3D casting models combined with professional knowledge for process reuse

Accurate retrieval of casting 3D models is crucial for process reuse. Current methods primarily focus on shape similarity, neglecting process design features, which compromises reusability. In this study, a novel deep learning retrieval method for process reuse was proposed, which integrates process design features into the retrieval of casting 3D models. This method leverages the comparative language-image pretraining (CLIP) model to extract shape features from the three views and sectional views of the casting model and combines them with process design features such as modulus, main wall thickness, symmetry, and length-to-height ratio to enhance process reusability. A database of 230 production casting models was established for model validation. Results indicate that incorporating process design features improves model accuracy by 6.09%, reaching 97.82%, and increases process similarity by 30.25%. The reusability of the process was further verified using the casting simulation software EasyCast. The results show that the process retrieved after integrating process design features produces the least shrinkage in the target model, demonstrating this method’s superior ability for process reuse. This approach does not require a large dataset for training and optimization, making it highly applicable to casting process design and related manufacturing processes.