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An energy consumption prediction approach of die casting machines driven by product parameters |
Erheng CHEN1, Hongcheng LI2, Huajun CAO1( ), Xuanhao WEN1 |
1. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China 2. School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunication, Chongqing 400065, China |
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Abstract Die casting machines, which are the core equipment of the machinery manufacturing industry, consume great amounts of energy. The energy consumption prediction of die casting machines can support energy consumption quota, process parameter energy-saving optimization, energy-saving design, and energy efficiency evaluation; thus, it is of great significance for Industry 4.0 and green manufacturing. Nevertheless, due to the uncertainty and complexity of the energy consumption in die casting machines, there is still a lack of an approach for energy consumption prediction that can provide support for process parameter optimization and product design taking energy efficiency into consideration. To fill this gap, this paper proposes an energy consumption prediction approach for die casting machines driven by product parameters. Firstly, the system boundary of energy consumption prediction is defined, and subsequently, based on the energy consumption characteristics analysis, a theoretical energy consumption model is established. Consequently, a systematic energy consumption prediction approach for die casting machines, involving product, die, equipment, and process parameters, is proposed. Finally, the feasibility and reliability of the proposed energy consumption prediction approach are verified with the help of three die casting machines and six types of products. The results show that the prediction accuracy of production time and energy consumption reached 91.64% and 85.55%, respectively. Overall, the proposed approach can be used for the energy consumption prediction of different die casting machines with different products.
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Keywords
die casting machine
energy consumption prediction
product parameters
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Corresponding Author(s):
Huajun CAO
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Just Accepted Date: 09 November 2021
Online First Date: 30 November 2021
Issue Date: 28 January 2022
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