Defects in titanium alloy castings for aerospace applications can greatly affect casting performance,resulting in high scrap rates.Casting defects arise from fluctuations in parameters during the casting process.To accurately and rapidly identify the effects of key process parameters on casting quality and reduce product defects,this study extracted process parameters from the Huzhou ERP system and used principal component analysis to reduce the parameter dimensions.BP neural network,random forest,and XGBoost models were compared for prediction and analysis.Results showed random forest had the highest prediction accuracy and recall.Associations between casting defects and process parameters were explained based on the predictive model to identify the root causes of defects and optimize the production process for effective defect reduction.The study demonstrates that predictive models incorporating big data analytics can monitor production in real-time to manage quality of aerospace castings,providing insights to facilitate smart manufacturing upgrades in the aerospace industry.
维普
万方数据