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Management of electrical energy in advanced manufacturing systems using machine learning

Grant number: 17/25987-3
Support type:Regular Research Grants
Duration: June 01, 2018 - May 31, 2020
Field of knowledge:Engineering - Production Engineering - Production Management
Principal Investigator:Fábio Lima
Grantee:Fábio Lima
Home Institution: Campus de São Bernardo do Campo. Centro Universitário da FEI (UNIFEI). Fundação Educacional Inaciana Padre Sabóia de Medeiros (FEI). São Bernardo do Campo , SP, Brazil
Assoc. researchers:Alexandre Augusto Massote ; João Chang Junior
Associated scholarship(s):19/12026-0 - Management of electrical energy in advanced manufacturing systems using machine learning, BP.TT


Recent supply difficulties by the Brazilian energy system, global climate change, pressure for the application of sustainable practices and costs of generation, transmission and distribution of electric power with a growth perspective are increasingly present factors in the discussion agenda of the modern society. In this scenario, several studies indicate that there is a significant potential for improvement of energy efficiency indicators in the manufacturing industry. In this way, the current productive processes must be concerned not only with the quality of the products developed, but also with their sustainability. There are manufacturing computer programs capable of managing and monitoring the energy consumed in the automated process. These programs are within a broader concept known as Digital Manufacturing. The digitization of manufacturing processes is the first step in achieving Advanced Manufacturing or Industry 4.0. However, because they are still relatively new tools, there is a great potential for study and research contributions in this segment. This project is designed to study and develop innovative and efficient energy management strategies in manufacturing systems using digital manufacturing tools. Moreover, in the context of advanced manufacturing, it will use in these solutions concepts of machine learning, more specifically Artificial Neural Networks (ANN), taking advantage of the data that will be generated in this new industrial scenario. (AU)