A machine learning approach applied to energy prediction in job shop environments
Nenhuma Miniatura disponível
Citações na Scopus
3
Tipo de produção
Artigo de evento
Data
2018
Autores
PEREIRA, M. S.
Fabio Lima
Fabio Lima
Orientador
Periódico
Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society
Título da Revista
ISSN da Revista
Título de Volume
Citação
PEREIRA, M. S.; LIMA F. A machine learning approach applied to energy prediction in job shop environments. Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, P. 2665-2670, dec. 2018.
Texto completo (DOI)
Palavras-chave
Resumo
© 2018 IEEE.Energy efficiency has become a great challenge for manufacturing companies. Although it is possible to improve efficiency applying new and more efficient machines, decision makers tend to look for some less expensive alternatives. In this context, the adoption of more efficient strategies during the production planning can allow the reduction in energy consumption and associated emissions. Furthermore, the current reality of manufacturing companies, brought by Industry 4.0 concepts, requires more flexibility of production systems, thus, increasing complexity for machine rescheduling without compromising sustainable requirements. In this paper, we propose a method to predict total energy consumption in job shop systems applying machine learning techniques. Different schedules may result in different consumption rates. However, there is a nonlinear relationship between these targets. Therefore, an Artificial Neural Network (ANN) is applied for a quick estimation of total energy consumption. In order to validate the model, computational experiments, using digital manufacturing software tools, are performed on different job shop configurations to show the efficiency of the proposed model.