Publication

Abstract :

Energy usage in industries is one of the major contributors for climate change, biodiversity loss and resource scarcity. Technological advancements in digitalization led by Industry 4.0 facilitates affordable energy monitoring systems. This allows comprehensive understanding of the primary energy needs and improvement in the areas of inefficiency of a modern manufacturing system. Machine learning has the potential to reveal untapped insights, providing decision support for sustainable manufacturing by improving environmental performances, significant savings, and operational opportunities. The objectives of this research paper are to develop a machine learning algorithm for characterization, and to estimate the energy consumption of various stages in 3D printing. Machine learning model is developed using long short-term memory algorithm, and is trained, validated, and deployed for the classification of various stages during 3D printing process. Furthermore, energy consumption in each stage is estimated based on Simpsons rule. The characterization of stages is useful for understanding the energy consumption in each stage during the 3D printing process and providing decision support to practitioners in improving the areas of energy and time inefficiencies.