Data-driven casting defect prediction model for sand casting based on random forest classification algorithm

The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality, resulting in a high scrap rate. A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency, which includes the random forest (RF) classification model, the feature importance analysis, and the process parameters optimization with Monte Carlo simulation. The collected data includes four types of defects and corresponding process parameters were used to construct the RF model. Classification results show a recall rate above 90