Real-time data-driven estimation of production for point bottom sealing and cutting machines using machine learning

Demand for sophisticated, data-driven techniques to improve production efficiency has increased due to the expansion of the packaging sector, especially in the packaging of polypropylene (PP) flexible materials. PP-based materials offer a variety of packaging applications due to their robust and versatile qualities, but they also require careful production planning to maximize time and resources. By examining important factors that affect output rates and manufacturing costs, such as material dimensions, thickness, and machine cutting speed, this study investigates how predictive modeling may transform production forecasting. This study attempts to build reliable models with optimized hyperpameters to forecast production yield by integrating simple Machine Learning (ML) approaches, such as Support Vector Regression (SVR), Artificial Neural Networks (ANN), Gaussian Process Regression (GPR), and ensemble based approaches such as Random Forest Regression (RFR), Gradient Boosting Regression (GBR), AdaBoost Regression (ABR), Bagging Regression (BR) and Extra Trees Regression (ETR). A performance based comparison of these models, revealed that the ensemble based models using GBR and BR outperformed the others. Further, the prediction performance was improvised by incorporating them as base models and training a voting regressor model. The usefulness of the prediction model has been further demonstrated in creation of reference charts for effective estimation of cost and runtime.