Energy consumption model and energy efficiency evaluation for CNC continuous generating grinding machine tools

Jiang, Ping; Li, Guolong; Pengxiang, Liu; Jiang, Lin; Li, Xiaozhuo

doi:10.1080/19397038.2017.1337253

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…It can be seen that numerical machine tools are responsible for almost 75% of the total industrial electricity consumption as shown in Figure 1 [1]. Numerical control machine tool is a kind of control technology that connects computer to the machine tool, and by controlling the movement of the machine tool, the computer can process the parts according to the shape and size on the drawing [2, 3]. It mainly includes the processing programme carrier, numerical control device, servo drive device, machine tool body, and other auxiliary devices [4].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Prediction of energy consumption of numerical control machine tools and analysis of key energy‐saving technologies

Han¹,

Ikbal

Khanna³

2021

IET Collab Intel Manufact

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The objective of this study is to ensure the sustainable development of traditional machinery manufacturing industry under limited conditions of non-renewable energy, and reduce the environmental pollution caused by high energy consumption of machine tools.Here the energy consumption of numerical control machine tools is analysed, and the relevant energy-saving model is established. The energy consumption system of the numerical control machinery tools, including the cutting parameters, is classified. The relevant energy consumption prediction model is built to evaluate the influence of different cutting parameters, feed per tooth, and the back cutting depth on the energy consumption of numerical control machine tools. The results show that classifying the energy consumption system of numerical control machine tools can effectively predict the energy consumption of machine tools, which provides theoretical support for the subsequent energy-saving experiments. Larger cutting parameters will increase the energy consumption of numerical control machine tools, improve the energy efficiency of machine tools at the same time. Thus, reducing the feed per tooth and the back cutting depth can effectively decrease its energy consumption. The results of this study provide data support and guidance for the energy-saving experiments and practical applications of numerical control machine tools.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Prediction of energy consumption of numerical control machine tools and analysis of key energy‐saving technologies

Han¹,

Ikbal

Khanna³

2021

IET Collab Intel Manufact

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“…The eco-efficiency can be achieved by an optimal selection of the main influencing grinding parameters and by considering surface roughness, costs and carbon footprint as the objectives of the optimization process [23]. Other works regarding eco-efficiency and energy consumption in grinding can be found in [24,25].…”

Section: Grindingmentioning

confidence: 99%

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

Renna

Materi

2021

Applied Sciences

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Climate change mitigation, the goal of reducing CO2 emissions, more stringent regulations and the increment in energy costs have pushed researchers to study energy efficiency and renewable energy sources. Manufacturing systems are large energy consumers and are thus responsible for huge greenhouse gas emissions; for these reasons, many studies have focused on this topic recently. This review aims to summarize the most important papers on energy efficiency and renewable energy sources in manufacturing systems published in the last fifteen years. The works are grouped together, considering the system typology, i.e., manufacturing system subclasses (single machine, flow shop, job shop, etc.) or the assembly line, the developed energy-saving policies and the implementation of the renewable energy sources in the studied contexts. A description of the main approaches used in the analyzed papers was discussed. The conclusion reports the main findings of the review and suggests future directions for the researchers in the integration of renewable energy in the manufacturing systems consumption models.

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“…This is because energy consumption is linked with environmental pollution and resource depletion. Almost all energy-generating processes pollute the environment in some way [12], and continuously increasing demand has made energy a scarce resource [13]. For energy efficiency improvement and energy saving in milling processes, much research has been done; for example, some methods include energy modeling and evaluation, as well as energy optimization for machining processes and machines [14].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Improvement of Greenness for Milling AL6061 Alloy through Life Cycle Assessment and Grey Relational Analysis

et al. 2022

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Modern manufacturing industries thrive on greenness, which means ensuring acceptable environmental impacts and required surface quality of the products during the manufacturing process. However, there is a conflict between surface quality and environmental performances in the milling process. The current research only considers energy consumption rather than total environmental impacts. In this respect, this research focuses on the multiobjective optimization of machining parameters for balancing the surface quality (i.e., surface roughness, Ra) and total environmental impact (TEI), which includes raw materials usage, energy consumption, and output pollutant emission during the milling of AL6061 alloy. First, life cycle assessment (LCA) of the milling process is used for evaluating the TEI. Then, multiobjective optimization is conducted using Grey Relational Analysis. The results indicated that the improvement of Ra and TEI can be achieved with higher cutting speed, higher depth, and wet conditions in milling. The optimization work showed that cutting speed of 165 m/min, feed rate of 0.28 mm/rev, depth of cut of 2 mm, and width of cut of 3 mm are the optimal combination among existing experiments. Compared to single objective optimization results, multiple responses (Ra and TEI) can be improved simultaneously.

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Energy consumption model and energy efficiency evaluation for CNC continuous generating grinding machine tools

Cited by 13 publications

References 9 publications

RETRACTED: Prediction of energy consumption of numerical control machine tools and analysis of key energy‐saving technologies

RETRACTED: Prediction of energy consumption of numerical control machine tools and analysis of key energy‐saving technologies

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

Evaluation and Improvement of Greenness for Milling AL6061 Alloy through Life Cycle Assessment and Grey Relational Analysis

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