A case study on the analysis of an injection moulding machine energy data sets for improving energy and production management

Panel: 2. Sustainable production towards a circular economy

This is a peer-reviewed paper.

Authors:
Julio Rezende, Limerick Institute of Technology, Brazil
John Cosgrove, Limerick Institute of Technology, Ireland
Samuel Carvalho, Limerick Institute of Technology, Ireland
Frank Doyle, Limerick Institute of Technology, Ireland

Abstract

Energy consumption is a concern worldwide, and energy efficiency approaches are among the pillars of Sustainable Manufacturing nowadays. Additionally, the industrial sector accounts for the largest share of energy use, being responsible for roughly 30% of all energy consumption worldwide. Due to developing and more restrict regulations towards energy efficiency, investing in this area presents big opportunities for industry such as reducing costs, increasing productivity and a significant reduction in environmental impact. Unfortunately, this engagement is still far from the desired level of development in many companies, especially small to medium enterprises (SMEs), who usually do not have the in-house expertise or the correct resources for applying such techniques.

However, with the developing technologies in industrial sector and the growth in the processing and storage capacity of IT equipment, industry has entered the age of "Big Data", where data collection and analysis play a major role in this scenario to acquire further knowledges towards energy efficiency and a better understanding of the production processes.

A study was carried out on a Thermoplastic Injection Moulding company, which segment is known for having an intense electrical energy usage given the nature of its production stages. In order to determine the productive and non-productive electrical energy embodied in manufacturing operations and get a better understanding of the production processes, an analysis based on the Machines’ time series data streams and some extra information about the processes was made. The outputs result in a better understanding of the machine’s electrical consumption, and also provide insights regarding potential saving strategies and improvements on the production side such as better scheduling, improved production tracking, operator engagement and equipment efficiency.

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