Energy consumption model for milling processes considering auxiliary load loss and its applications
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With the modern manufacturing industry evolving and advancing and amid a more energy conscious society, high energy demand in manufacturing—particularly in machining—has drawn more and more attention. Accurate energy consumption modelling is critical to the improvement of energy efficiency in machining. In the existing energy models of machining processes, typically the so-called auxiliary load loss (which is whatever the difference between the total energy consumption and the amount of energy consumed due to the physical cutting of material and the idle running of the machine tool) is not considered. However, the auxiliary load loss could account for as much as 15–20% of the total energy consumption; simply ignoring it or making crude assumption on it would obviously lead to a less accurate energy consumption model. In this paper, a new energy consumption model for milling that takes full account of the auxiliary load loss is proposed. The proposed energy consumption model is crucially based on an empirical validation work that correctly characterizes the auxiliary power loss as a quadratic function of the cutting power. Cutting experiments are then carried out that convincingly confirm the correctness of the proposed energy consumption model. Moreover, as an application of the proposed energy consumption model, we perform two deep slot cutting experiments and show how our new energy model can help find the best process parameter—the number of intermediate layers to cut—that will consume the minimum amount of energy, which is as much as more than 52% less than that without considering the auxiliary load loss. It is expected that the proposed energy consumption model will have its application in minimization of energy consumption to a wider range of machining tasks, not limited to only the simplest deep slot cutting operation.
KeywordsEnergy consumption modelling Energy efficiency Auxiliary load losses Cutting power Energy saving
This study is co-supported by the Major National Science and Technology Projects (No. 2017ZX04011430), the Shanxi Provincial Key Research and Development Program (No. 2018ZDXM-GY-063).
- 8.Costa DMD, Brito TG, de Paiva AP, Leme RC, Balestrassi PP (2016) A normal boundary intersection with multivariate mean square error approach for dry end milling process optimization of the AISI 1045 steel. J Clean Prod 135:1658–1672. https://doi.org/10.1016/j.jclepro.2016.01.062 CrossRefGoogle Scholar
- 9.Camposeco-Negrete C, Nájera JDC, Miranda-Valenzuela JC (2016) Optimization of cutting parameters to minimize energy consumption during turning of AISI 1018 steel at constant material removal rate using robust design. Int J Adv Manuf Technol 83(5-8):1341–1347. https://doi.org/10.1007/s00170-015-7679-9 CrossRefGoogle Scholar
- 16.Diaz N, Choi S, Helu M, Chen Y, Jayanathan S, Yasui Y, Kong D, Pavanaskar S, Dornfeld D (2010) Machine tool design and operation strategies for green manufacturing. https://escholarship.org/uc/item/5gz7j6rn