Intelligent energy-based product quality control in the injection molding process

Abstract

Energy is a critical resource for powering modern society, supporting economic growth, and meeting basic human needs. In fact, energy efficiency is an important factor for companies looking to remain competitive in the market. This need is particularly more important in energy-intensive industries, such as plastics manufacturing by process injection molding, where energy costs can account for a significant portion of the overall operating costs. By investing in smart energy-efficient technologies based on artificial intelligence tools and on good practices manufacturing, companies can reduce their energy consumption, improve their environmental performance, and enhance their sustainability. In this paper, an artificial neural network, trained on experimental dataset, has been used for modelling the relationships between energy consumption, product quality, and process setting parameters. Then, an energy control system has been building in Matlab Simulink to simulate the behaviour of real production process of polypropylene product and to identify the optimal process settings that achieve the desired level of product quality while controlling energy consumption. The proposed system demonstrated its effectiveness in the case study adopted and then can be used in others similar plastics production. Moreover, its approach can be used to develop the smart control systems for others industrial processes.