Energy efficiency evaluation for machining systems through virtual part

Tuo, Junbo; Liu, Fei; Liu, Peiji; Zhang, Hua; Cai, Wei

doi:10.1016/j.energy.2018.06.096

Cited by 38 publications

(10 citation statements)

References 24 publications

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“…Especially for the manufacturing process, a considerable amount of researches focus on energy monitoring, modeling, and energy-saving methods [25][26][27][28][29][30]. Various previous studies indicate that the energy efficiency of machining is not high enough [31][32][33], which is generally lower than thirty percent [34,35]. Hence, the potential of energy-saving for machining process is very considerable [36].…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Rapid Power and Energy Prediction Method of Drilling Process for Sustainable Manufacturing

Jia

Zhang

et al. 2021

IEEE Access

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Energy modeling and energy-saving have attracted extensive attention in the manufacturing industry. Energy monitoring, modeling, and management issues of grinding, milling, and turning processes have been widely studied. However, special research on drilling power and energy consumption needs to be strengthened. The existing drilling power and energy models have the problems of high computational complexity and low practicability. To address this issue, an improved rapid power and energy prediction method of drilling process is proposed in this study. The motivation of the proposed method is to reduce the computational complexity and improve the practicability without losing the predictive accuracy for drilling power and energy. To verify the effectiveness of the proposed method, experimental and case studies were carried out. The results show that the number of formulas, variables, coefficients of the proposed method are all decreased significantly, therefore, the computational complexity is greatly reduced. Meanwhile, power predictive accuracy is improved by 1.91% instead of decreasing compared with the traditional method. Consequently, the simpler model, lower computational complexity, and higher power accuracy make the proposed method more practical in manufacturing industry.INDEX TERMS drilling processes; power prediction; energy prediction; sustainable manufacturing.

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Section: Literature Reviewmentioning

confidence: 99%

An Improved Rapid Power and Energy Prediction Method of Drilling Process for Sustainable Manufacturing

Jia

Zhang

et al. 2021

IEEE Access

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“…Running status acquisition is a precondition to determine the influence of running statuses on carbon emission of machine tools [37]. For some CNC machine tools, running statuses can be obtained from CNC systems though computer communication technology if its communication protocol is open.…”

Section: Data Processingmentioning

confidence: 99%

Dynamic Acquisition and Real-Time Distribution of Carbon Emission for Machining Through Mining Energy Data

Tuo

Liu

2019

IEEE Access

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The acquisition and distribution of carbon emission in manufacturing process are usually regarded as a precondition for management and decision making of carbon emission reduction in mechanical manufacturing industry. Current researches regarding acquisition of carbon emission for mechanical manufacture mainly derive from static statistics and are insufficient to reflect interactive influence of running statues and production processes on carbon emission in mechanical processing. This paper proposed a dynamic acquisition of carbon emission for mechanical manufacture, especially for machining process, which contributes to carbon emission monitoring and management in manufacturing process and overcomes existing deficiencies in static statistic methods. Furthermore, a dynamic monitoring system, consisting of single-machine and multi-machine levels, is developed to verify the feasibility of the proposed method, and is applied in a machining workshop. Case study shows that the proposed method is valid to obtain dynamic variation and distribution of carbon emission, to determine influence of running statues or production processes on emission carbon and to explore potential emission reduction strategies.

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“…For instance, in one year, a machine tool emits CO 2 equivalent to 61 sports utility vehicles [9]. Machine tools are enlisted as one of the critical products in the European Union's Eco-design directive 2009/125/EC to meet the carbon emission standard [10]. The literature reveals that the average energy efficiency of the machine tools is less than 30% [11].…”

Section: Introductionmentioning

confidence: 99%

“…γ c(10) where C p is a constant and α, β and γ are coefficients of cutting speed, feed rate and cutting depth respectively. In the constant-power consumption machining process, the process parameters i.e.…”

mentioning

confidence: 99%

Development of an Empirical Model for Variable Power Consumption Machining Processes: A Case of End Facing

2021

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Machining processes contribute significantly to the energy consumption of manufacturing industries, and reducing their energy consumption is a major challenge to achieve sustainable and cleaner manufacturing. The accurate and practical energy consumption prediction models for a machine tool are the foundation for sustainable and cleaner manufacturing. The machining of a workpiece mainly involves constant-power consumption machining processes e.g. turning and variablepower consumption machining processes e.g. end facing. The cutting power characteristics of the variable-power consumption machining processes are more complex and dynamic, due to change in one of the process parameters (e.g. cutting speed during end facing) than the constant-power consumption machining processes, and have received limited attention in the literature. In the present work, an empirical model is developed to predict the cutting energy consumption of the variable-power consumption machining process i.e. end facing. The end facing experiments were performed on a Computer Numerical Control Lathe in the dry and wet environment to obtain the fitting constants of the developed model. Four validation experiments were performed to confirm the prediction capability of the developed model. The validation experiments confirm that the accuracy of the developed model is more than 96%. Further, the predicted power profiles were in good agreement with the measured power profiles, which shows that the developed model satisfactorily encompasses the influences of the process parameters on the cutting power consumption.

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Energy efficiency evaluation for machining systems through virtual part

Cited by 38 publications

References 24 publications

An Improved Rapid Power and Energy Prediction Method of Drilling Process for Sustainable Manufacturing

An Improved Rapid Power and Energy Prediction Method of Drilling Process for Sustainable Manufacturing

Dynamic Acquisition and Real-Time Distribution of Carbon Emission for Machining Through Mining Energy Data

Development of an Empirical Model for Variable Power Consumption Machining Processes: A Case of End Facing

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