Management of electrical energy in advanced manufacturing systems using machine learning

• Grant number: 17/25987-3
• Support Opportunities: Regular Research Grants
• Start date: June 01, 2018
• End date: May 31, 2020
• Field of knowledge: Engineering - Production Engineering - Production Management
• Principal Investigator: Fábio Lima
• Grantee: Fábio Lima
• Host Institution: Centro Universitário FEI (UNIFEI). Campus de São Bernardo do Campo. São Bernardo do Campo , SP, Brazil
• City of the host institution: São Bernardo do Campo
• Associated 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

Abstract

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)

Scientific publications

(4)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

LOPES JUNIOR, MARCOS MANOEL; DE MATTOS, CLAUDIA APARECIDA; LIMA, FABIO. Toward Cleaner Production by Evaluating Opportunities of Saving Energy in a Short-Cycle Time Flowshop. SUSTAINABILITY, v. 16, n. 6, p. 23-pg., 2024-03-01. (17/25987-3)

PEREIRA, MOISES S.; LIMA, FABIO; IEEE. A Machine Learning Approach Applied to Energy Prediction in Job Shop Environments. IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, v. N/A, p. 6-pg., 2018-01-01. (17/25987-3)

LIMA, FABIO; MASSOTE, ALEXANDRE AUGUSTO; MAIA, RODRIGO FILEV; IEEE. IoT Energy Retrofit and the Connection of Legacy Machines Inside the Industry 4.0 Concept. 45TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2019), v. N/A, p. 6-pg., 2019-01-01. (17/25987-3)

PEREIRA, MOISES SANTANA; AQUINO JR, PLINIO THOMAZ; DE MATTOS, CLAUDIA APARECIDA; LIMA, FABIO. Energy Estimation and Production Scheduling in Job Shop Using Machine Learning. IEEE ACCESS, v. 12, p. 13-pg., 2024-01-01. (17/25987-3)