Abstract
Digitalisation is currently developing in many sectors of the economy. The success of a manufacturing enterprise requires a transition to digital control of production and business processes, to the greatest extent possible without human intervention using artificial intelligence technologies. This research applies a Manufacturing Execution System (MES) with Predictive Analysis to an automatic production line (Chemical Line) which contains sensors, actuators and pumps controlled by four Programmable Logic Controllers, linked together, and being monitored through a Supervisory Control and Data Acquisition system. The production line composed of four different subsystems, responsible for filtration to bottling the chemical product. This paper tries to join the MES system with Artificial Neural Networks (ANN) in order to not only monitor the system but having predictive analysis to plan the future. In such way we will take advantage of the benefits of the ANN, such as Long-Short Term Memory architectures. The experimental data will be compared with other usual platforms, such as the Master SCADA itself, through the course of this research.
Highlights
Introduction and description of manufacturing execution system A Manufacturing Execution System is a computerised system that tracks and monitors all the elements involved in the transformation of raw materials to the final product and documents these information in order to have full reports of all elements involved in the process
Manufacturing Execution System (MES) system can help us to make decisions for the future in order to optimise our process to achieve better results. These results can be obtained from the production line in real time, and can be plotted using various types of software, but in order to make prediction on them for the future prospect, we can take advantage of Artificial Neural Networks to make a perfectly good regression in order to see our gains and losses in the future, and be able to optimise our process
The concept in this paper acts like a client-server system where the main part of it is the MES
Summary
With the rapid increase in technology, it is almost guaranteed that paper based operating systems that documents all assets of these enterprises will be replaced by more efficient and reliable smart MES [1]. MES system can help us to make decisions for the future in order to optimise our process to achieve better results. These results can be obtained from the production line in real time, and can be plotted using various types of software, but in order to make prediction on them for the future prospect, we can take advantage of Artificial Neural Networks to make a perfectly good regression in order to see our gains and losses in the future, and be able to optimise our process
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