TY - GEN
T1 - Using grammatical evolution for modelling energy consumption on a computer numerical control machine
AU - Carvalho, Samuel
AU - Sullivan, Joe
AU - Dias, Douglas Mota
AU - Naredo, Enrique
AU - Ryan, Conor
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/7/7
Y1 - 2021/7/7
N2 - Discrete manufacturing is known to be a high consumer of energy and much work has been done in continuous improvement and energy saving methods addressing this issue. Computer Numerical Control (CNC) machines, commonly used in the manufacturing of metal parts, are highly energy-demanding because of many required sub-systems, such as cooling, lubrication, logical interfaces and electric motors. For this reason, there is a large body of work focusing on modelling the energy needs of this class of machine. This paper applies Grammatical Evolution (GE) for developing auto-regressive models for the energy consumption of a CNC machine. Empirical data from three 24-hour work shifts comprising three different types of products are used as inputs. We also introduce an autocorrelation-informed approach for the grammar, which benefits from a prior analysis of the training data for better capturing periodic or close to periodic behaviour. Finally, we compare the outcomes from real and predicted energy profiles through the use of an existing analysis tool, which is capable of extracting production-related information such as total and average KW consumption, number of parts produced and breakdown of production and idle hours. Results show that GE yields accurate and explainable models for the analysed scenario.
AB - Discrete manufacturing is known to be a high consumer of energy and much work has been done in continuous improvement and energy saving methods addressing this issue. Computer Numerical Control (CNC) machines, commonly used in the manufacturing of metal parts, are highly energy-demanding because of many required sub-systems, such as cooling, lubrication, logical interfaces and electric motors. For this reason, there is a large body of work focusing on modelling the energy needs of this class of machine. This paper applies Grammatical Evolution (GE) for developing auto-regressive models for the energy consumption of a CNC machine. Empirical data from three 24-hour work shifts comprising three different types of products are used as inputs. We also introduce an autocorrelation-informed approach for the grammar, which benefits from a prior analysis of the training data for better capturing periodic or close to periodic behaviour. Finally, we compare the outcomes from real and predicted energy profiles through the use of an existing analysis tool, which is capable of extracting production-related information such as total and average KW consumption, number of parts produced and breakdown of production and idle hours. Results show that GE yields accurate and explainable models for the analysed scenario.
KW - CNC machines
KW - energy consumption
KW - grammatical evolution
KW - real-world applications
UR - http://www.scopus.com/inward/record.url?scp=85111065524&partnerID=8YFLogxK
U2 - 10.1145/3449726.3463185
DO - 10.1145/3449726.3463185
M3 - Conference contribution
AN - SCOPUS:85111065524
T3 - GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion
SP - 1557
EP - 1563
BT - GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2021 Genetic and Evolutionary Computation Conference, GECCO 2021
Y2 - 10 July 2021 through 14 July 2021
ER -