Energy consumption modeling and prediction of the milling process: A multistage perspective

Zhang, Chaoyong; Zhou, Zhiheng; Tian, Guangdong; Xie, Yang; Huang, Zhengtao

doi:10.1177/0954405416682278

Cited by 17 publications

(9 citation statements)

References 32 publications

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“…Second, substituting 16 sets of data in Table 3 into equation 10 The prediction error comparison of tool tip cutting specific energy model shown in equations (13) and 14is shown in Figure 6. Table 4 is used to verify the prediction accuracy of the models.…”

Section: Verification Of the Developed Tool Tip Cutting Specific Enermentioning

confidence: 99%

“…Zhou et al 12 established an energy-consumption prediction model of plane grinder processing system based on BP neural network, with the wheel speed, feed speed of worktable, and grinding depth as input, which can predict the energy consumption of the grinder well. Zhang et al 13 developed an energy-consumption prediction model for milling process from multi-stage perspective with improved gene expression programming algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tool tip cutting specific energy prediction model and the influence of machining parameters and tool wear in milling

Zhao

Zheng

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Most of the existing energy-consumption models of machine tools are related to specific machine components and hence cannot be applied to other machine tools with different specifications. In order to help operators optimize machining parameters for improving energy efficiency, the tool tip cutting specific energy prediction model based on machining parameters and tool wear in milling is developed, which is independent of the standby power of machine tools and the spindle no-load power. Then, the prediction accuracy of the proposed model is verified with dry milling AISI 1045 steel experiments. Finally, the influence of machining parameters and tool wear on tool tip cutting specific energy is studied. The developed model is independent of machine components, so it can reveal the influence of machining parameters and tool wear on tool tip cutting specific energy. The tool tip cutting specific energy reduces with the increase in the cutting depth, side cutting depth, feed rate, and cutting speed, while increases linearly as the tool wears gradually. The research results are helpful to formulate efficient and energy-saving processing schemes on various milling machines.

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Section: Verification Of the Developed Tool Tip Cutting Specific Enermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tool tip cutting specific energy prediction model and the influence of machining parameters and tool wear in milling

Zhao

Zheng

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…In addition, various studies have been conducted regarding the milling process and energy consumption. Zhang et al [24] developed energy consumption modeling and a prediction model of milling processes. They used multiple linear regressions, a sliding filter, and variable neighborhood search-based gene expression programming to model energy consumption.…”

Section: B Prediction and Analysis On Energy Datamentioning

confidence: 99%

An Integrated Energy Data Analytics Approach for Machine Tools

Kang

Lee

2020

IEEE Access

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Efficiency and the reduction of energy consumption in the manufacturing industry are crucial issues for green and sustainable manufacturing. In particular, the machine tool field, which is a representative resources of the manufacturing industry and consumes a large amount of energy, requires the development of a technology capable of monitoring and analyzing data from an energy point of view in addition to existing manufacturing indicators such as productivity improvement and processing quality; however, existing studies have focused on one-dimensional areas such as monitoring energy consumption or developing predictive models in an experimental environment. Therefore, it is necessary to establish a practical and integrated environment by developing more systematic methodologies and supporting systems. In this paper, an integrated approach to the energy consumption of machine tools based on a data analysis is introduced. During the development of the approach, the real-time data processing methods of various sources, such as sensors and computer numerical control interfaces were investigated in a real environment, and the energy consumption models of the target machine's tools and feeds were defined. An energy data-based comparative analysis method for diagnosing an abnormal state was also developed. In addition, practicality was confirmed through the implementation of the developed approach and the actual application process. Through the results of this paper, a more efficient energy data management and consumption plan can be established, and the convergence of advanced information communication technology can be expected to be the basis for green and sustainable manufacturing. INDEX TERMS Data analytics, energy consumption unit, machine tools, monitoring and prediction.

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“…For instance, Mia et al 21 performed intelligent optimization of the machining process from the perspective of smart manufacturing. On the other side, the response surface methodology (RSM) was applied to analyze the impacts of input process parameters on energy consumption 22 , power consumption, cutting force, and surface roughness 23 . Similarly, the multi-objective optimizations were performed in order to minimize the energy consumption in the dry turning of stainless steel 24 .…”

Section: Introductionmentioning

confidence: 99%

Green machining for the dry milling process of stainless steel 304

Nguyen

Mia

Dang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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Dry machining represents an eco-friendly method that reduces the environmental impacts, saves energy costs, and protects operator health. This article presents a multi-response optimization which aims to enhance the power factor and decrease the energy consumption as well as the surface roughness for the dry machining of a stainless steel 304. The cutting speed ( V), depth of cut ( a), feed rate ( f), and nose radius ( r) were the processing conditions. The outputs of the optimization are the power factor, energy consumption, and surface roughness. The relationships between inputs and outputs were established using the radial basis function models. The experimental data were normalized, with the use of the Grey relational analysis. The principal component analysis is applied to calculate the weight values of technical responses. The desirability approach is used to observe the optimal values. The results showed that the technical outputs are primarily influenced by the feed rate and cutting speed. The reductions of energy consumption and surface roughness are approximately 34.85% and 57.65%, respectively, and the power factor improves around 28.83%, compared to the initial process parameter settings. The outcomes and findings of the investigated work can be used for further research in sustainable design and manufacturing as well as directly used in the knowledge-based and expert systems for dry milling applications in industrial practices.

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Energy consumption modeling and prediction of the milling process: A multistage perspective

Cited by 17 publications

References 32 publications

Tool tip cutting specific energy prediction model and the influence of machining parameters and tool wear in milling

Tool tip cutting specific energy prediction model and the influence of machining parameters and tool wear in milling

An Integrated Energy Data Analytics Approach for Machine Tools

Green machining for the dry milling process of stainless steel 304

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