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

Abstract

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% for all categories. The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model. Finally, the classification model was applied to different production conditions for quality prediction. In the case of process parameters optimization for gas porosity defects, this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution. The prediction model, when applied to the factory, greatly improved the efficiency of defect detection. Results show that the scrap rate decreased from 10.16% to 6.68%.