Exergy-based Energy Efficiency Evaluation Model for Machine Tools Considering Thermal Stability

Li, Benjie; Cao, Huajun; Hon, Bernard; Liu, Lei; Gao, Xi

doi:10.1007/s40684-020-00204-8

Cited by 18 publications

(12 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But that was for the moving components of the CNC. Another work has provided the energy consumption model by Li et al [17], but that work was particularly dedicated for hand to process material turning. Similarly, Hu et al [18] have proposed a model energy efficiency evaluation model based on the energy that has been considered the thermal stability.…”

Section: Literature Reviewmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

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

View full text Add to dashboard Cite

show abstract

“…Through the above analysis and literature [35,36] research, it can be seen that CNC instructions determine the motion mode of CNC machine tools. CNC machine tools have different energy consumption characteristics under different CNC instructions (such as G00 and G01, etc.).…”

Section: Analysis Of Energy Consumption Characteristics Of Cnc Machin...mentioning

confidence: 97%

A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

et al. 2021

View full text Add to dashboard Cite

Accurate and rapid prediction of the energy consumption of CNC machining is an effective means to realize the lean management of CNC machine tools energy consumption as well as to achieve the sustainable development of the manufacturing industry. Aiming at the drawbacks of existing CNC milling energy consumption prediction methods in terms of efficiency and precision, a novel milling energy consumption prediction method based on program parsing and parallel neural network is proposed. Firstly, the relationship between CNC program and energy consumption of CNC machine tool is analyzed. Based on the structural characteristics of the CNC program, an automatic parsing algorithm for the CNC program is proposed. Moreover, based on the improved parallel neural network, the mapping relationship between the energy consumption parameters of each CNC instruction and the milling energy consumption is constructed. Finally, the proposed method is compared with the literature to verify the superiority of the proposed method in terms of prediction efficiency and accuracy, and the practicability of the method is verified through the case study. The proposed method lays the foundation for efficient and low-consumption process planning and energy efficiency improvement of machine tools and is conducive to the sustainable development of the environment.

show abstract

“…Where η weld = welding energy efficiency, E weld = welding energy, E elec = welding electrical energy demand, P weld = welding power, P elec = welding electrical power demand, t = time. Li et al (2021) took this basic relationship and broke it down according to the specific processes involved in machining, identifying two basic types of energy input and two basic types of energy output (Equation ( 4)), providing visibility on not just how much energy is input, but where it is inputted to.…”

Section: Energy Calculation In Manufacturing Systemsmentioning

confidence: 99%

“…A further concept discussed by both Zhou et al (2016) and Li et al (2021) is the "Specific Energy Consumption "(SEC), measured in J/kg. This is a productivity measure, which is related to the Material Removal Rate (MRR), measured in kg/hr, where "process productivity" is defined as the relationship between the SEC and the MRR.…”

Section: Specific Processing Energy and Productivitymentioning

confidence: 99%

Mathematical modelling for energy efficiency improvement in laser welding

Goffin

Jones

Tyrer

et al. 2021

Journal of Cleaner Production

View full text Add to dashboard Cite

Within the global manufacturing industry, there is increasing recognition of the need to improve energy efficiency, to reduce both costs and carbon footprint. Significant work has sought to improve the energy efficiency of a variety of different processes, but in the case of laser welding, research has primarily focussed on the lasermaterial interaction and not on energy rationalisation. This work addresses this knowledge gap by methodically investigating and analysing the energy requirements of a laser welding process. In this study, a mathematical model has been created in order to take a "whole-system" approach to laser welding electrical demand, accounting for all component sub-systems of the laser cell. This model was then experimentally tested via use of an electrical energy monitor to gather energy data for an autogenous welding process carried out on 316L stainless steel at a variety of parameters. Mathematical analysis of this data was then used to create a matrix of electrical power draws at different test conditions, which was further developed into an analysis of process productivity. This revealed a very strong non-linear inverse correlation between process rate (kg/hr) and specific energy consumption (J/kg). This process productivity measurement was placed in context with other manufacturing processes, revealing that laser welding has a relatively high process rate (1E-02 -1E0 kg/hr), and relatively low energy consumption (1E+08 and 1E+09 J/kg) in comparison. A further benefit of this model is that it allows the selection of processing parameters according to their energy demand. Energy flow analysis allows the direct comparison of the energy demand of different sets of processing parameters, which metallurgical analysis shows produce similar welds. In this study, it was shown that parameter selection alone was capable of producing an electrical energy saving of 60% for a given weld. This emphasises the importance of parameter selection and control in enabling environmentally cleaner manufacturing without compromising part quality or the need for investment in capital equipment.

show abstract

Exergy-based Energy Efficiency Evaluation Model for Machine Tools Considering Thermal Stability

Cited by 18 publications

References 46 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 Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

Mathematical modelling for energy efficiency improvement in laser welding

Contact Info

Product

Resources

About