Machine learning based prediction of production using real time data of a point bottom sealing and cutting machine

Abstract

The packaging sector utilizes polypropylene based flexible materials for diverse product packaging with customization options in size and design achieved through advanced flexographic printing and point bottom sealing and cutting machines. Accurately estimating production time and quantity is vital for efficient planning and cost estimation, with factors like material dimensions, thickness, and cutting machine speed influencing production output. Understanding the intricate relationship between these parameters is essential for comprehending their impact on production time and quantity. Predicting production quantity before production begins helps in determining machine runtime and associated costs. In large-scale production systems, machine learning (ML) has proven to be a useful tool for resource allocation and predictive scheduling. An attempt has been made in this paper to develop an intelligent model for predicting the yield of a cutting machine using artificial neural network (ANN), support vector regression (SVR), regression tree ensemble (RTE) and gaussian process regression (GPR). The most crucial features for prediction were identified and the hyperparameters of the ML models were optimized to create efficient models for prediction. A comparative analysis of the four models revealed that the GPR model was simple and effective with least training time and prediction error.